Repository: reiniscimurs/DRL-robot-navigation-IR-SIM Branch: master Commit: 31e1a4d511bb Files: 81 Total size: 44.5 MB Directory structure: gitextract_12_7tajc/ ├── .devin/ │ └── wiki.json ├── .github/ │ └── workflows/ │ └── main.yml ├── .gitignore ├── README.md ├── docs/ │ ├── api/ │ │ ├── IR-SIM/ │ │ │ ├── ir-marl-sim.md │ │ │ └── ir-sim.md │ │ ├── Testing/ │ │ │ ├── test.md │ │ │ └── testrnn.md │ │ ├── Training/ │ │ │ ├── train.md │ │ │ └── trainrnn.md │ │ ├── Utils/ │ │ │ ├── replay_buffer.md │ │ │ └── utils.md │ │ ├── models/ │ │ │ ├── DDPG.md │ │ │ ├── HCM.md │ │ │ ├── MARL/ │ │ │ │ ├── Attention.md │ │ │ │ └── TD3.md │ │ │ ├── PPO.md │ │ │ ├── RCPG.md │ │ │ ├── SAC.md │ │ │ ├── TD3.md │ │ │ ├── __init__.md │ │ │ └── cnntd3.md │ │ └── robot_nav.md │ └── index.md ├── mkdocs.yml ├── pyproject.toml ├── robot_nav/ │ ├── SIM_ENV/ │ │ ├── __init__.py │ │ ├── marl_sim.py │ │ ├── sim.py │ │ └── sim_env.py │ ├── __init__.py │ ├── assets/ │ │ └── data.yml │ ├── eval_points.yaml │ ├── marl_test_random.py │ ├── marl_test_single.py │ ├── marl_train.py │ ├── models/ │ │ ├── CNNTD3/ │ │ │ ├── CNNTD3.py │ │ │ └── __init__.py │ │ ├── DDPG/ │ │ │ ├── DDPG.py │ │ │ └── __init__.py │ │ ├── HCM/ │ │ │ ├── __init__.py │ │ │ └── hardcoded_model.py │ │ ├── MARL/ │ │ │ ├── Attention/ │ │ │ │ ├── __init__.py │ │ │ │ ├── g2anet.py │ │ │ │ └── iga.py │ │ │ ├── __init__.py │ │ │ └── marlTD3/ │ │ │ ├── __init__.py │ │ │ └── marlTD3.py │ │ ├── PPO/ │ │ │ ├── PPO.py │ │ │ └── __init__.py │ │ ├── RCPG/ │ │ │ ├── RCPG.py │ │ │ └── __init__.py │ │ ├── SAC/ │ │ │ ├── SAC.py │ │ │ ├── SAC_actor.py │ │ │ ├── SAC_critic.py │ │ │ ├── SAC_utils.py │ │ │ └── __init__.py │ │ ├── TD3/ │ │ │ ├── TD3.py │ │ │ └── __init__.py │ │ └── __init__.py │ ├── replay_buffer.py │ ├── rl_test.py │ ├── rl_test_random.py │ ├── rl_train.py │ ├── rnn_test.py │ ├── rnn_train.py │ ├── rvo_test_random.py │ ├── rvo_test_single.py │ ├── utils.py │ └── worlds/ │ ├── circle_world.yaml │ ├── cross_world.yaml │ ├── eval_world.yaml │ ├── multi_robot_world.yaml │ └── robot_world.yaml └── tests/ ├── __init__.py ├── test_data.yml ├── test_marl_world.yaml ├── test_model.py ├── test_sim.py ├── test_utils.py └── test_world.yaml ================================================ FILE CONTENTS ================================================ ================================================ FILE: .devin/wiki.json ================================================ { "repo_notes": [ { "content": "" } ], "pages": [ { "title": "Overview", "purpose": "Introduce the DRL-robot-navigation-IR-SIM project, explaining its purpose as a Deep Reinforcement Learning system for robot navigation using the IR-SIM simulator, and providing a high-level architecture overview", "page_notes": [ { "content": "" } ] }, { "title": "Getting Started", "purpose": "Guide users through installation using Poetry, running the main training script, and monitoring with TensorBoard", "parent": "Overview", "page_notes": [ { "content": "" } ] }, { "title": "Core Training System", "purpose": "Explain the main training orchestration, including the training loop structure (epochs, episodes, steps), state-action-reward cycle, and integration with models and environments", "page_notes": [ { "content": "" } ] }, { "title": "Simulation Environments", "purpose": "Document the SIM (single-agent) and MARL_SIM (multi-agent) environment wrappers around IR-SIM, including state observation processing, reward functions, and reset mechanics", "parent": "Core Training System", "page_notes": [ { "content": "" } ] }, { "title": "Data Management and Replay Buffers", "purpose": "Describe the replay buffer implementations (ReplayBuffer, RolloutReplayBuffer, RolloutBuffer), the get_buffer factory function, and experience storage/sampling strategies for different algorithms", "parent": "Core Training System", "page_notes": [ { "content": "" } ] }, { "title": "Single-Agent Models", "purpose": "Overview of the single-agent DRL model library, explaining the model taxonomy, evolutionary relationships (DDPG→TD3→CNNTD3→RCPG), and shared interfaces", "page_notes": [ { "content": "" } ] }, { "title": "TD3 and DDPG", "purpose": "Document the Twin Delayed DDPG algorithm and base DDPG, including network architectures, training loop with target smoothing and delayed updates, and state preparation for 10-dimensional states", "parent": "Single-Agent Models", "page_notes": [ { "content": "" } ] }, { "title": "CNNTD3", "purpose": "Explain the CNN-enhanced TD3 model designed for 185-dimensional laser scan data, detailing the 1D convolutional layers for spatial feature extraction and its role as the primary model in rl_train.py", "parent": "Single-Agent Models", "page_notes": [ { "content": "" } ] }, { "title": "RCPG", "purpose": "Document the Recurrent Convolutional Policy Gradient model that adds LSTM/GRU/RNN layers to CNNTD3 for temporal modeling, including its specialized RolloutReplayBuffer for sequence sampling", "parent": "Single-Agent Models", "page_notes": [ { "content": "" } ] }, { "title": "SAC", "purpose": "Describe the Soft Actor-Critic algorithm with entropy regularization, including the DiagGaussianActor with SquashedNormal distribution and DoubleQCritic architecture", "parent": "Single-Agent Models", "page_notes": [ { "content": "" } ] }, { "title": "PPO", "purpose": "Explain the Proximal Policy Optimization on-policy algorithm, its clipped objective, RolloutBuffer for episodic collection, and action standard deviation decay mechanism", "parent": "Single-Agent Models", "page_notes": [ { "content": "" } ] }, { "title": "HCM (Hardcoded Model)", "purpose": "Document the hard-coded navigation model used for collecting collision data and pre-training datasets, including its action computation logic and sample saving functionality", "parent": "Single-Agent Models", "page_notes": [ { "content": "" } ] }, { "title": "Multi-Agent Reinforcement Learning", "purpose": "Introduce the MARL capabilities of the system, explaining the multi-agent simulation environment, attention-based coordination mechanisms, and training differences from single-agent approaches", "page_notes": [ { "content": "" } ] }, { "title": "MARL Simulation Environment", "purpose": "Detail the MARL_SIM class for multi-robot scenarios, including per-agent state processing, two-phase reward functions, connection matrices for communication topology, and conflict-free reset logic", "parent": "Multi-Agent Reinforcement Learning", "page_notes": [ { "content": "" } ] }, { "title": "marlTD3 with Attention Mechanisms", "purpose": "Explain the multi-agent TD3 implementation with hard-soft attention, the Actor and Critic architectures with attention encoders, In-Graph Attention and G2ANET, GoalAttentionLayer message passing, and auxiliary losses (BCE, entropy)", "parent": "Multi-Agent Reinforcement Learning", "page_notes": [ { "content": "" } ] }, { "title": "Environment Configuration", "purpose": "Document the YAML configuration system for world setup, robot properties, sensor specifications (lidar2d), obstacle definitions, and evaluation point configurations", "page_notes": [ { "content": "" } ] }, { "title": "Testing and Validation", "purpose": "Overview of the comprehensive testing infrastructure using pytest, including unit tests for models, environments, and utilities, with CI/CD integration via GitHub Actions", "page_notes": [ { "content": "" } ] }, { "title": "Model Testing", "purpose": "Explain the test suite for validating model training capabilities, including parameterized tests for all algorithms, prefilled test data usage, and max bound variant testing", "parent": "Testing and Validation", "page_notes": [ { "content": "" } ] }, { "title": "Simulation and Environment Testing", "purpose": "Document tests for SIM and MARL_SIM environments, including state retrieval, step execution, reset functionality, and the cossin utility function", "parent": "Testing and Validation", "page_notes": [ { "content": "" } ] }, { "title": "Buffer and Utility Testing", "purpose": "Detail the testing framework for the get_buffer factory function, validating correct buffer type instantiation (ReplayBuffer, RolloutReplayBuffer, RolloutBuffer) based on model type", "parent": "Testing and Validation", "page_notes": [ { "content": "" } ] }, { "title": "Advanced Features", "purpose": "Document advanced capabilities including max upper bound loss for Q-value regularization, pretraining from offline datasets, and model-specific architectural enhancements", "page_notes": [ { "content": "" } ] }, { "title": "Max Upper Bound Loss", "purpose": "Explain the optional Q-value bounding mechanism available for TD3, DDPG, and CNNTD3 to address overestimation bias, including the theoretical maximum calculation and loss formulation", "parent": "Advanced Features", "page_notes": [ { "content": "" } ] }, { "title": "Pretraining and Offline Learning", "purpose": "Document the Pretraining class for loading offline experience data from YAML files, the get_max_bound utility, and integration with the get_buffer factory for model initialization", "parent": "Advanced Features", "page_notes": [ { "content": "" } ] }, { "title": "Development and Deployment", "purpose": "Cover the development workflow including Poetry dependency management, project structure with pyproject.toml, CI/CD pipeline with GitHub Actions, and TensorBoard integration for training monitoring", "page_notes": [ { "content": "" } ] } ] } ================================================ FILE: .github/workflows/main.yml ================================================ name: Run Tests on: push: branches: [master] pull_request: branches: [master] jobs: test: runs-on: ubuntu-latest steps: - name: Checkout code uses: actions/checkout@v3 - name: Set up Python uses: actions/setup-python@v4 with: python-version: '3.10' - name: Install Poetry run: | curl -sSL https://install.python-poetry.org | python3 - echo "$HOME/.local/bin" >> $GITHUB_PATH - name: Install dependencies run: poetry install - name: Run tests run: poetry run pytest ================================================ FILE: .gitignore ================================================ /.venv/ /runs/ /site/ ================================================ FILE: README.md ================================================ [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/reiniscimurs/DRL-robot-navigation-IR-SIM) **DRL Robot navigation in IR-SIM** Deep Reinforcement Learning algorithm implementation for simulated robot navigation in IR-SIM. Using 2D laser sensor data and information about the goal point a robot learns to navigate to a specified point in the environment. ![Example](https://github.com/reiniscimurs/DRL-robot-navigation-IR-SIM/blob/master/out.gif) **Installation** * Package versioning is managed with poetry \ `pip install poetry` * Clone the repository \ `git clone https://github.com/reiniscimurs/DRL-robot-navigation.git` * Navigate to the cloned location and install using poetry \ `poetry install` **Training the model** * Run the training by executing the train.py file \ `poetry run python robot_nav/rl_train.py` * To open tensorbord, in a new terminal execute \ `tensorboard --logdir runs` **Sources** | Package | Description | Source | |:--------|:-------------------------------------------------------------:|------------------------------------:| | IR-SIM | Light-weight robot simulator | https://github.com/hanruihua/ir-sim | | PythonRobotics | Python code collection of robotics algorithms (Path planning) | https://github.com/AtsushiSakai/PythonRobotics | **Models** | Model | Description | Model Source | |:-----------------|:-----------------------------------------------------------------------------------------------:|----------------------------------------------------------:| | TD3 | Twin Delayed Deep Deterministic Policy Gradient model | https://github.com/reiniscimurs/DRL-Robot-Navigation-ROS2 | | SAC | Soft Actor-Critic model | https://github.com/denisyarats/pytorch_sac | | PPO | Proximal Policy Optimization model | https://github.com/nikhilbarhate99/PPO-PyTorch | | DDPG | Deep Deterministic Policy Gradient model | Updated from TD3 | | CNNTD3 | TD3 model with 1D CNN encoding of laser state | - | | RCPG | Recurrent Convolution Policy Gradient - adding recurrence layers (lstm/gru/rnn) to CNNTD3 model | - | | MARL: TD3-G2ANet | G2ANet attention encoder for TD3 model in MARL setting | G2ANet adapted from https://github.com/starry-sky6688/MARL-Algorithms | | MARL: TD3-IGS | In-Graph Softmax attention model for TD3 model in MARL setting | - | **Max Upper Bound Models** Models that support the additional loss of Q values exceeding the maximal possible Q value in the episode. Q values that exceed this upper bound are used to calculate a loss for the model. This helps to control the overestimation of Q values in off-policy actor-critic networks. To enable max upper bound loss set `use_max_bound = True` when initializing a model. | Model | |:-------| | TD3 | | DDPG | | CNNTD3 | **MARL Models** Multi agent RL setting with training multiple robots a the same time with a single policy. Implementation does not use sensor informaition and only exchanged graph messages are used for navigation. Read more bout the In-Graph Softmax for MARL implementation [here](https://medium.com/@reinis_86651/learning-graphs-for-marl-on-the-fly-9fe44c356808). Video of results: [![EXPERIMENTS](https://img.youtube.com/vi/SGl7sil_dpo/0.jpg)](https://www.youtube.com/watch?v=SGl7sil_dpo) ================================================ FILE: docs/api/IR-SIM/ir-marl-sim.md ================================================ # MARL-IR-SIM ::: robot_nav.SIM_ENV.marl_sim options: show_root_heading: true show_source: true ================================================ FILE: docs/api/IR-SIM/ir-sim.md ================================================ # IR-SIM ::: robot_nav.SIM_ENV.sim options: show_root_heading: true show_source: true ================================================ FILE: docs/api/Testing/test.md ================================================ # Testing ::: robot_nav.test options: show_root_heading: true show_source: true ::: robot_nav.test_random options: show_root_heading: true show_source: true ================================================ FILE: docs/api/Testing/testrnn.md ================================================ # Testing RNN ::: robot_nav.test_rnn options: show_root_heading: true show_source: true ================================================ FILE: docs/api/Training/train.md ================================================ # Training ::: robot_nav.train options: show_root_heading: true show_source: true ================================================ FILE: docs/api/Training/trainrnn.md ================================================ # Training RNN ::: robot_nav.train_rnn options: show_root_heading: true show_source: true ================================================ FILE: docs/api/Utils/replay_buffer.md ================================================ # Replay/Rollout Buffer ::: robot_nav.replay_buffer options: show_root_heading: true show_source: true ================================================ FILE: docs/api/Utils/utils.md ================================================ # Utils ::: robot_nav.utils options: show_root_heading: true show_source: true ================================================ FILE: docs/api/models/DDPG.md ================================================ # DDPG ::: robot_nav.models.DDPG.DDPG options: show_root_heading: true show_source: true ================================================ FILE: docs/api/models/HCM.md ================================================ # Hardcoded Model ::: robot_nav.models.HCM.hardcoded_model options: show_root_heading: true show_source: true ================================================ FILE: docs/api/models/MARL/Attention.md ================================================ # Hard-Soft Attention ::: robot_nav.models.MARL.hardsoftAttention options: show_root_heading: true show_source: true ================================================ FILE: docs/api/models/MARL/TD3.md ================================================ # MARL TD3 ::: robot_nav.models.MARL.marlTD3 options: show_root_heading: true show_source: true ================================================ FILE: docs/api/models/PPO.md ================================================ # PPO ::: robot_nav.models.PPO.PPO options: show_root_heading: true show_source: true ================================================ FILE: docs/api/models/RCPG.md ================================================ # RCPG ::: robot_nav.models.RCPG.RCPG options: show_root_heading: true show_source: true ================================================ FILE: docs/api/models/SAC.md ================================================ # SAC ::: robot_nav.models.SAC.SAC options: show_root_heading: true show_source: true ::: robot_nav.models.SAC.SAC_actor options: show_root_heading: true show_source: true ::: robot_nav.models.SAC.SAC_critic options: show_root_heading: true show_source: true ::: robot_nav.models.SAC.SAC_utils options: show_root_heading: true show_source: true ================================================ FILE: docs/api/models/TD3.md ================================================ # TD3 ::: robot_nav.models.TD3.TD3 options: show_root_heading: true show_source: true ================================================ FILE: docs/api/models/__init__.md ================================================ # __init__ ## Documentation ``` No documentation available for __init__. ``` ================================================ FILE: docs/api/models/cnntd3.md ================================================ # CNNTD3 ::: robot_nav.models.CNNTD3.CNNTD3 options: show_root_heading: true show_source: true ================================================ FILE: docs/api/robot_nav.md ================================================ # Robot Navigation Will be updated ::: robot_nav options: show_root_heading: true show_source: true ================================================ FILE: docs/index.md ================================================ # Welcome to DRL-robot-navigation-IR-SIM **DRL Robot navigation in IR-SIM** Deep Reinforcement Learning algorithm implementation for simulated robot navigation in IR-SIM. Using 2D laser sensor data and information about the goal point a robot learns to navigate to a specified point in the environment. **Installation** * Package versioning is managed with poetry \ `pip install poetry` * Clone the repository \ `git clone https://github.com/reiniscimurs/DRL-robot-navigation.git` * Navigate to the cloned location and install using poetry \ `poetry install` **Training the model** * Run the training by executing the train.py file \ `poetry run python robot_nav/train.py` * To open tensorbord, in a new terminal execute \ `tensorboard --logdir runs` **Sources** | Package | Description | Source | |:--------|:-------------------------------------------------------------:|------------------------------------:| | IR-SIM | Light-weight robot simulator | https://github.com/hanruihua/ir-sim | | PythonRobotics | Python code collection of robotics algorithms (Path planning) | https://github.com/AtsushiSakai/PythonRobotics | **Models** | Model | Description | Model Source | |:----------|:-----------------------------------------------------------------------------------------------:|----------------------------------------------------------:| | TD3 | Twin Delayed Deep Deterministic Policy Gradient model | https://github.com/reiniscimurs/DRL-Robot-Navigation-ROS2 | | SAC | Soft Actor-Critic model | https://github.com/denisyarats/pytorch_sac | | PPO | Proximal Policy Optimization model | https://github.com/nikhilbarhate99/PPO-PyTorch | | DDPG | Deep Deterministic Policy Gradient model | Updated from TD3 | | CNNTD3 | TD3 model with 1D CNN encoding of laser state | - | | RCPG | Recurrent Convolution Policy Gradient - adding recurrence layers (lstm/gru/rnn) to CNNTD3 model | - | **Max Upper Bound Models** Models that support the additional loss of Q values exceeding the maximal possible Q value in the episode. Q values that exceed this upper bound are used to calculate a loss for the model. This helps to control the overestimation of Q values in off-policy actor-critic networks. To enable max upper bound loss set `use_max_bound = True` when initializing a model. | Model | |:-------| | TD3 | | DDPG | | CNNTD3 | ================================================ FILE: mkdocs.yml ================================================ extra: python_path: - ./robot_nav nav: - Home: index.md - API Reference: IR-SIM: - SIM: api/IR-SIM/ir-sim - MARL SIM: api/IR-SIM/ir-marl-sim Models: - DDPG: api/models/DDPG.md - TD3: api/models/TD3.md - CNNTD3: api/models/cnntd3.md - RCPG: api/models/RCPG.md - HCM: api/models/HCM.md - PPO: api/models/PPO.md - SAC: api/models/SAC.md - MARL: - HardSoft Attention: api/models/MARL/Attention - TD3: api/models/MARL/TD3 Training: - Train: api/Training/train.md - Train RNN: api/Training/trainrnn.md Testing: - Test: api/Testing/test.md - Test RNN: api/Testing/testrnn.md Utils: - Replay Buffer: api/Utils/replay_buffer.md - Utils: api/Utils/utils.md plugins: - search - mkdocstrings site_name: DRL-robot-navigation-IR-SIM theme: name: material ================================================ FILE: pyproject.toml ================================================ [project] name = "robot-nav" version = "0.1.0" description = "" authors = [ {name = "reinis",email = "ireinisi@gmail.com"} ] readme = "README.md" requires-python = ">=3.10" dependencies = [ "ir-sim[all] (>=2.3.0,<3.0.0)", "torch (>=2.5.1,<3.0.0)", "tensorboard (>=2.18.0,<3.0.0)", "tqdm (>=4.67.1,<5.0.0)", "pytest (>=8.3.4,<9.0.0)", "mkdocs (>=1.6.1,<2.0.0)", "mkdocstrings[python] (>=0.29.1,<0.30.0)", "mkdocs-material (>=9.6.11,<10.0.0)", "scipy (>=1.15.2,<2.0.0)", "torch-geometric (>=2.6.1,<3.0.0)", ] [tool.pytest.ini_options] testpaths = ["tests"] [build-system] requires = ["poetry-core>=2.0.0,<3.0.0"] build-backend = "poetry.core.masonry.api" ================================================ FILE: robot_nav/SIM_ENV/__init__.py ================================================ ================================================ FILE: robot_nav/SIM_ENV/marl_sim.py ================================================ import irsim import numpy as np import random import torch from robot_nav.SIM_ENV.sim_env import SIM_ENV class MARL_SIM(SIM_ENV): """ Simulation environment for multi-agent robot navigation using IRSim. This class extends the SIM_ENV and provides a wrapper for multi-robot simulation and interaction, supporting reward computation and custom reset logic. Attributes: env (object): IRSim simulation environment instance. robot_goal (np.ndarray): Current goal position(s) for the robots. num_robots (int): Number of robots in the environment. x_range (tuple): World x-range. y_range (tuple): World y-range. """ def __init__( self, world_file="multi_robot_world.yaml", disable_plotting=False, reward_phase=1, ): """ Initialize the MARL_SIM environment. Args: world_file (str, optional): Path to an IRSim YAML world configuration. Defaults to "multi_robot_world.yaml". disable_plotting (bool, optional): If True, disables all IRSim plotting and display windows. Useful for headless training. Defaults to False. reward_phase (int, optional): Selects the reward function variant used by `get_reward`. Supported values: {1, 2}. Defaults to 1. """ display = False if disable_plotting else True self.env = irsim.make( world_file, disable_all_plot=disable_plotting, display=display ) robot_info = self.env.get_robot_info(0) self.robot_goal = robot_info.goal self.num_robots = len(self.env.robot_list) self.x_range = self.env._world.x_range self.y_range = self.env._world.y_range self.reward_phase = reward_phase def step(self, action, connection, combined_weights=None): """ Perform a simulation step for all robots using the provided actions and connections. Args: action (list): A list of length `num_robots`. Each element is `[linear_velocity, angular_velocity]` to be applied to the corresponding robot for this step. Units follow IRSim's kinematics setup (typically m/s and rad/s). connection (Tensor): A torch.Tensor of shape `(num_robots, num_robots-1)` containing *logits* that indicate pairwise connections per robot *without* the self-connection column. This is not used for logic here, but preserved for compatibility (see commented code); you may use it upstream to form `combined_weights`. combined_weights (Tensor or None, optional): A torch.Tensor of shape `(num_robots, num_robots)` **or** `(num_robots, num_robots-1)` that encodes visualization weights for robot-to-robot edges. When provided, edges from robot *i* to *j* are drawn with line width `weight * 2`. If you pass the `(num_robots, num_robots-1)` form, ensure indexing aligns with how you constructed it (self-column typically omitted). Returns: tuple: ( poses (list[list[float]]): `[x, y, theta]` for each robot after the step. distances (list[float]): Euclidean distance from each robot to its goal. coss (list[float]): Cosine of the angle between robot heading and goal vector. sins (list[float]): Sine of the angle between robot heading and goal vector. collisions (list[bool]): Collision flags for each robot, as reported by IRSim. goals (list[bool]): Arrival flags for each robot. If True this step, a new random goal was scheduled for that robot. action (list): Echo of the input `action`. rewards (list[float]): Per-robot rewards computed by `get_reward(...)`. positions (list[list[float]]): `[x, y]` positions for each robot after the step. goal_positions (list[list[float]]): `[x, y]` goal coordinates for each robot after any arrival updates this step. ) """ self.env.step(action_id=[i for i in range(self.num_robots)], action=action) self.env.render() poses = [] distances = [] coss = [] sins = [] collisions = [] goals = [] rewards = [] positions = [] goal_positions = [] robot_states = [ [self.env.robot_list[i].state[0], self.env.robot_list[i].state[1]] for i in range(self.num_robots) ] for i in range(self.num_robots): robot_state = self.env.robot_list[i].state closest_robots = [ np.linalg.norm( [ robot_states[j][0] - robot_state[0], robot_states[j][1] - robot_state[1], ] ) for j in range(self.num_robots) if j != i ] robot_goal = self.env.robot_list[i].goal goal_vector = [ robot_goal[0].item() - robot_state[0].item(), robot_goal[1].item() - robot_state[1].item(), ] distance = np.linalg.norm(goal_vector) goal = self.env.robot_list[i].arrive pose_vector = [np.cos(robot_state[2]).item(), np.sin(robot_state[2]).item()] cos, sin = self.cossin(pose_vector, goal_vector) collision = self.env.robot_list[i].collision action_i = action[i] reward = self.get_reward( goal, collision, action_i, closest_robots, distance, self.reward_phase ) position = [robot_state[0].item(), robot_state[1].item()] goal_position = [robot_goal[0].item(), robot_goal[1].item()] distances.append(distance) coss.append(cos) sins.append(sin) collisions.append(collision) goals.append(goal) rewards.append(reward) positions.append(position) poses.append( [robot_state[0].item(), robot_state[1].item(), robot_state[2].item()] ) goal_positions.append(goal_position) if combined_weights is not None: i_weights = combined_weights[i].tolist() for j in range(self.num_robots): if combined_weights is not None: weight = i_weights[j] # else: # weight = 1 other_robot_state = self.env.robot_list[j].state other_pos = [ other_robot_state[0].item(), other_robot_state[1].item(), ] rx = [position[0], other_pos[0]] ry = [position[1], other_pos[1]] self.env.draw_trajectory( np.array([rx, ry]), refresh=True, linewidth=weight * 2 ) if goal: self.env.robot_list[i].set_random_goal( obstacle_list=self.env.obstacle_list, init=True, range_limits=[ [self.x_range[0] + 1, self.y_range[0] + 1, -3.141592653589793], [self.x_range[1] - 1, self.y_range[1] - 1, 3.141592653589793], ], ) return ( poses, distances, coss, sins, collisions, goals, action, rewards, positions, goal_positions, ) def reset( self, robot_state=None, robot_goal=None, random_obstacles=False, random_obstacle_ids=None, ): """ Reset the simulation environment and optionally set robot and obstacle positions. Args: robot_state (list or None, optional): Initial robot state(s) as [[x], [y], [theta]]. If None, random states are assigned ensuring minimum spacing between robots. robot_goal (list or None, optional): Fixed goal position(s) for robots. If None, random goals are generated within the environment boundaries. random_obstacles (bool, optional): If True, reposition obstacles randomly within bounds. random_obstacle_ids (list or None, optional): IDs of obstacles to randomize. Defaults to seven obstacles starting from index equal to the number of robots. Returns: tuple: ( poses (list): List of [x, y, theta] for each robot, distances (list): Distance to goal for each robot, coss (list): Cosine of angle to goal for each robot, sins (list): Sine of angle to goal for each robot, collisions (list): All False after reset, goals (list): All False after reset, action (list): Initial action ([[0.0, 0.0], ...]), rewards (list): Rewards for initial state, positions (list): Initial [x, y] for each robot, goal_positions (list): Initial goal [x, y] for each robot, ) """ if robot_state is None: robot_state = [[random.uniform(3, 9)], [random.uniform(3, 9)], [0]] init_states = [] for robot in self.env.robot_list: conflict = True while conflict: conflict = False robot_state = [ [random.uniform(3, 9)], [random.uniform(3, 9)], [random.uniform(-3.14, 3.14)], ] pos = [robot_state[0][0], robot_state[1][0]] for loc in init_states: vector = [ pos[0] - loc[0], pos[1] - loc[1], ] if np.linalg.norm(vector) < 0.6: conflict = True init_states.append(pos) robot.set_state( state=np.array(robot_state), init=True, ) if random_obstacles: if random_obstacle_ids is None: random_obstacle_ids = [i + self.num_robots for i in range(7)] self.env.random_obstacle_position( range_low=[self.x_range[0], self.y_range[0], -3.14], range_high=[self.x_range[1], self.y_range[1], 3.14], ids=random_obstacle_ids, non_overlapping=True, ) for robot in self.env.robot_list: if robot_goal is None: robot.set_random_goal( obstacle_list=self.env.obstacle_list, init=True, range_limits=[ [self.x_range[0] + 1, self.y_range[0] + 1, -3.141592653589793], [self.x_range[1] - 1, self.y_range[1] - 1, 3.141592653589793], ], ) else: self.env.robot.set_goal(np.array(robot_goal), init=True) self.env.reset() self.robot_goal = self.env.robot.goal action = [[0.0, 0.0] for _ in range(self.num_robots)] con = torch.tensor( [[0.0 for _ in range(self.num_robots - 1)] for _ in range(self.num_robots)] ) poses, distance, cos, sin, _, _, action, reward, positions, goal_positions = ( self.step(action, con) ) return ( poses, distance, cos, sin, [False] * self.num_robots, [False] * self.num_robots, action, reward, positions, goal_positions, ) @staticmethod def get_reward(goal, collision, action, closest_robots, distance, phase=1): """ Compute the reward for a single robot based on goal progress, collisions, control effort, and proximity to other robots. Args: goal (bool): Whether the robot reached its goal. collision (bool): Whether the robot collided with an obstacle or another robot. action (list): [linear_velocity, angular_velocity] applied by the robot. closest_robots (list): Distances to other robots. distance (float): Current distance to the goal. phase (int, optional): Reward configuration (1 or 2). Default is 1. Returns: float: Computed scalar reward. Raises: ValueError: If an unknown reward phase is specified. """ match phase: case 1: if goal: return 100.0 elif collision: return -100.0 * 3 * action[0] else: r_dist = 1.5 / distance cl_pen = 0 for rob in closest_robots: add = (1.25 - rob) ** 2 if rob < 1.25 else 0 cl_pen += add return action[0] - 0.5 * abs(action[1]) - cl_pen + r_dist case 2: if goal: return 70.0 elif collision: return -100.0 * 3 * action[0] else: cl_pen = 0 for rob in closest_robots: add = (3 - rob) ** 2 if rob < 3 else 0 cl_pen += add return -0.5 * abs(action[1]) - cl_pen case _: raise ValueError("Unknown reward phase") ================================================ FILE: robot_nav/SIM_ENV/sim.py ================================================ import irsim import numpy as np import random from robot_nav.SIM_ENV.sim_env import SIM_ENV class SIM(SIM_ENV): """ A simulation environment interface for robot navigation using IRSim. This class wraps around the IRSim environment and provides methods for stepping, resetting, and interacting with a mobile robot, including reward computation. Attributes: env (object): The simulation environment instance from IRSim. robot_goal (np.ndarray): The goal position of the robot. """ def __init__(self, world_file="robot_world.yaml", disable_plotting=False): """ Initialize the simulation environment. Args: world_file (str): Path to the world configuration YAML file. disable_plotting (bool): If True, disables rendering and plotting. """ display = False if disable_plotting else True self.env = irsim.make( world_file, disable_all_plot=disable_plotting, display=display ) robot_info = self.env.get_robot_info(0) self.robot_goal = robot_info.goal def step(self, lin_velocity=0.0, ang_velocity=0.1): """ Perform one step in the simulation using the given control commands. Args: lin_velocity (float): Linear velocity to apply to the robot. ang_velocity (float): Angular velocity to apply to the robot. Returns: (tuple): Contains the latest LIDAR scan, distance to goal, cosine and sine of angle to goal, collision flag, goal reached flag, applied action, and computed reward. """ self.env.step(action_id=0, action=np.array([[lin_velocity], [ang_velocity]])) self.env.render() scan = self.env.get_lidar_scan() latest_scan = scan["ranges"] robot_state = self.env.get_robot_state() goal_vector = [ self.robot_goal[0].item() - robot_state[0].item(), self.robot_goal[1].item() - robot_state[1].item(), ] distance = np.linalg.norm(goal_vector) goal = self.env.robot.arrive pose_vector = [np.cos(robot_state[2]).item(), np.sin(robot_state[2]).item()] cos, sin = self.cossin(pose_vector, goal_vector) collision = self.env.robot.collision action = [lin_velocity, ang_velocity] reward = self.get_reward(goal, collision, action, latest_scan) return latest_scan, distance, cos, sin, collision, goal, action, reward def reset( self, robot_state=None, robot_goal=None, random_obstacles=True, random_obstacle_ids=None, ): """ Reset the simulation environment, optionally setting robot and obstacle states. Args: robot_state (list or None): Initial state of the robot as a list of [x, y, theta, speed]. robot_goal (list or None): Goal state for the robot. random_obstacles (bool): Whether to randomly reposition obstacles. random_obstacle_ids (list or None): Specific obstacle IDs to randomize. Returns: (tuple): Initial observation after reset, including LIDAR scan, distance, cos/sin, and reward-related flags and values. """ if robot_state is None: robot_state = [[random.uniform(1, 9)], [random.uniform(1, 9)], [0]] self.env.robot.set_state( state=np.array(robot_state), init=True, ) if random_obstacles: if random_obstacle_ids is None: random_obstacle_ids = [i + 1 for i in range(7)] self.env.random_obstacle_position( range_low=[0, 0, -3.14], range_high=[10, 10, 3.14], ids=random_obstacle_ids, non_overlapping=True, ) if robot_goal is None: self.env.robot.set_random_goal( obstacle_list=self.env.obstacle_list, init=True, range_limits=[[1, 1, -3.141592653589793], [9, 9, 3.141592653589793]], ) else: self.env.robot.set_goal(np.array(robot_goal), init=True) self.env.reset() self.robot_goal = self.env.robot.goal action = [0.0, 0.0] latest_scan, distance, cos, sin, _, _, action, reward = self.step( lin_velocity=action[0], ang_velocity=action[1] ) return latest_scan, distance, cos, sin, False, False, action, reward @staticmethod def get_reward(goal, collision, action, laser_scan): """ Calculate the reward for the current step. Args: goal (bool): Whether the goal has been reached. collision (bool): Whether a collision occurred. action (list): The action taken [linear velocity, angular velocity]. laser_scan (list): The LIDAR scan readings. Returns: (float): Computed reward for the current state. """ if goal: return 100.0 elif collision: return -100.0 else: r3 = lambda x: 1.35 - x if x < 1.35 else 0.0 return action[0] - abs(action[1]) / 2 - r3(min(laser_scan)) / 2 ================================================ FILE: robot_nav/SIM_ENV/sim_env.py ================================================ from abc import ABC, abstractmethod import numpy as np class SIM_ENV(ABC): @abstractmethod def step(self): raise NotImplementedError("step method must be implemented by subclass.") @abstractmethod def reset(self): raise NotImplementedError("reset method must be implemented by subclass.") @staticmethod def cossin(vec1, vec2): """ Compute the cosine and sine of the angle between two 2D vectors. Args: vec1 (list): First 2D vector. vec2 (list): Second 2D vector. Returns: (tuple): (cosine, sine) of the angle between the vectors. """ vec1 = vec1 / np.linalg.norm(vec1) vec2 = vec2 / np.linalg.norm(vec2) cos = np.dot(vec1, vec2) sin = vec1[0] * vec2[1] - vec1[1] * vec2[0] return cos, sin @staticmethod @abstractmethod def get_reward(): raise NotImplementedError("get_reward method must be implemented by subclass.") ================================================ FILE: robot_nav/__init__.py ================================================ ================================================ FILE: robot_nav/assets/data.yml ================================================ [File too large to display: 40.2 MB] ================================================ FILE: robot_nav/eval_points.yaml ================================================ robot: poses: - [[3], [4], [0], [0]] - [[8], [1], [1], [0]] - [[2], [6], [1], [0]] - [[7], [1], [0], [0]] - [[7], [6.5], [2], [0]] - [[9], [9], [3], [0]] - [[2], [9], [1], [0]] - [[3], [6], [3], [0]] - [[1], [7], [0], [0]] - [[5], [7], [3], [0]] goals: - [[8], [8], [0]] - [[2], [9], [0]] - [[7], [1], [0]] - [[7.2], [9], [0]] - [[1], [1], [0]] - [[5], [1], [0]] - [[7], [4], [0]] - [[9], [4], [0]] - [[1], [9], [0]] - [[5], [1], [0]] #robot_states = [np.array([[3],[4],[0],[0]]), # np.array([[8],[1],[1],[0]]), # np.array([[2],[6],[1],[0]]), # np.array([[7],[1],[0],[0]]), # np.array([[7],[6.5],[2],[0]]), # np.array([[9],[9],[3],[0]]), # np.array([[2], [9], [1], [0]]), # np.array([[3], [6], [3], [0]]), # np.array([[1], [7], [0], [0]]), # np.array([[5], [7], [3], [0]]), # ] # robot_goals = [ # np.array([[8], [8], [0]]), # np.array([[2], [9], [0]]), # np.array([[7], [1], [0]]), # np.array([[7.2], [9], [0]]), # np.array([[1], [1], [0]]), # np.array([[5], [1], [0]]), # np.array([[7], [4], [0]]), # np.array([[9], [4], [0]]), # np.array([[1], [9], [0]]), # np.array([[5], [1], [0]]), # ] ================================================ FILE: robot_nav/marl_test_random.py ================================================ import statistics from pathlib import Path from tqdm import tqdm import matplotlib.pyplot as plt from robot_nav.models.MARL.marlTD3.marlTD3 import TD3 import torch import numpy as np from robot_nav.SIM_ENV.marl_sim import MARL_SIM def outside_of_bounds(poses): """ Check if any robot is outside the defined world boundaries. Args: poses (list): List of [x, y, theta] poses for each robot. Returns: bool: True if any robot is outside the 21x21 area centered at (6, 6), else False. """ outside = False for pose in poses: norm_x = pose[0] - 6 norm_y = pose[1] - 6 if abs(norm_x) > 10.5 or abs(norm_y) > 10.5: outside = True break return outside def main(args=None): """Main training function""" # ---- Hyperparameters and setup ---- action_dim = 2 # number of actions produced by the model max_action = 1 # maximum absolute value of output actions state_dim = 11 # number of input values in the neural network (vector length of state input) device = torch.device( "cuda" if torch.cuda.is_available() else "cpu" ) # using cuda if it is available, cpu otherwise epoch = 1 # starting epoch number episode = 0 max_steps = 600 # maximum number of steps in single episode steps = 0 # starting step number save_every = 5 # save the model every n training cycles test_scenarios = 1000 # ---- Instantiate simulation environment and model ---- sim = MARL_SIM( world_file="worlds/multi_robot_world.yaml", disable_plotting=True, reward_phase=2, ) # instantiate environment model = TD3( state_dim=state_dim, action_dim=action_dim, max_action=max_action, num_robots=sim.num_robots, device=device, save_every=save_every, load_model=True, model_name="TDR-MARL-test", load_model_name="TDR-MARL-train", load_directory=Path("robot_nav/models/MARL/marlTD3/checkpoint"), attention="igs", ) # instantiate a model connections = torch.tensor( [[0.0 for _ in range(sim.num_robots - 1)] for _ in range(sim.num_robots)] ) # ---- Take initial step in environment ---- poses, distance, cos, sin, collision, goal, a, reward, positions, goal_positions = ( sim.step([[0, 0] for _ in range(sim.num_robots)], connections) ) # get the initial step state running_goals = 0 running_reward = 0 running_collisions = 0 running_timesteps = 0 reward_per_ep = [] goals_per_ep = [] col_per_ep = [] lin_actions = [] ang_actions = [] entropy_list = [] epsilon = 1e-6 pbar = tqdm(total=test_scenarios) while episode < test_scenarios: state, terminal = model.prepare_state( poses, distance, cos, sin, collision, a, goal_positions ) action, connection, combined_weights = model.get_action( np.array(state), False ) # get an action from the model combined_weights_norm = combined_weights / ( combined_weights.sum(dim=-1, keepdim=True) + epsilon ) # Entropy for analysis/logging entropy = ( ( -(combined_weights_norm * (combined_weights_norm + epsilon).log()) .sum(dim=-1) .mean() ) .data.cpu() .numpy() ) entropy_list.append(entropy) a_in = [ [(a[0] + 1) / 4, a[1]] for a in action ] # clip linear velocity to [0, 0.5] m/s range ( poses, distance, cos, sin, collision, goal, a, reward, positions, goal_positions, ) = sim.step( a_in, connection, combined_weights ) # get data from the environment running_goals += sum(goal) running_collisions += sum(collision) / 2 running_reward += sum(reward) running_timesteps += 1 for j in range(len(a_in)): lin_actions.append(a_in[j][0].item()) ang_actions.append(a_in[j][1].item()) outside = outside_of_bounds(poses) if ( sum(collision) > 0.5 or steps == max_steps or outside ): # reset environment of terminal state reached, or max_steps were taken ( poses, distance, cos, sin, collision, goal, a, reward, positions, goal_positions, ) = sim.reset() reward_per_ep.append(running_reward) running_reward = 0 goals_per_ep.append(running_goals) running_goals = 0 col_per_ep.append(running_collisions) running_collisions = 0 steps = 0 episode += 1 pbar.update(1) else: steps += 1 reward_per_ep = np.array(reward_per_ep, dtype=np.float32) goals_per_ep = np.array(goals_per_ep, dtype=np.float32) col_per_ep = np.array(col_per_ep, dtype=np.float32) lin_actions = np.array(lin_actions, dtype=np.float32) ang_actions = np.array(ang_actions, dtype=np.float32) entropy = np.array(entropy_list, dtype=np.float32) avg_ep_reward = statistics.mean(reward_per_ep) avg_ep_reward_std = statistics.stdev(reward_per_ep) avg_ep_col = statistics.mean(col_per_ep) avg_ep_col_std = statistics.stdev(col_per_ep) avg_ep_goals = statistics.mean(goals_per_ep) avg_ep_goals_std = statistics.stdev(goals_per_ep) mean_lin_action = statistics.mean(lin_actions) lin_actions_std = statistics.stdev(lin_actions) mean_ang_action = statistics.mean(ang_actions) ang_actions_std = statistics.stdev(ang_actions) mean_entropy = statistics.mean(entropy) mean_entropy_std = statistics.stdev(entropy) print(f"avg_ep_reward: {avg_ep_reward}") print(f"avg_ep_reward_std: {avg_ep_reward_std}") print(f"avg_ep_col: {avg_ep_col}") print(f"avg_ep_col_std: {avg_ep_col_std}") print(f"avg_ep_goals: {avg_ep_goals}") print(f"avg_ep_goals_std: {avg_ep_goals_std}") print(f"mean_lin_action: {mean_lin_action}") print(f"lin_actions_std: {lin_actions_std}") print(f"mean_ang_action: {mean_ang_action}") print(f"ang_actions_std: {ang_actions_std}") print(f"mean_entropy: {mean_entropy}") print(f"mean_entropy_std: {mean_entropy_std}") print("..............................................") model.writer.add_scalar("test/avg_ep_reward", avg_ep_reward, epoch) model.writer.add_scalar("test/avg_ep_reward_std", avg_ep_reward_std, epoch) model.writer.add_scalar("test/avg_ep_col", avg_ep_col, epoch) model.writer.add_scalar("test/avg_ep_col_std", avg_ep_col_std, epoch) model.writer.add_scalar("test/avg_ep_goals", avg_ep_goals, epoch) model.writer.add_scalar("test/avg_ep_goals_std", avg_ep_goals_std, epoch) model.writer.add_scalar("test/mean_lin_action", mean_lin_action, epoch) model.writer.add_scalar("test/lin_actions_std", lin_actions_std, epoch) model.writer.add_scalar("test/mean_ang_action", mean_ang_action, epoch) model.writer.add_scalar("test/ang_actions_std", ang_actions_std, epoch) model.writer.add_scalar("test/mean_entropy", mean_entropy, epoch) model.writer.add_scalar("test/mean_entropy_std", mean_entropy_std, epoch) bins = 100 model.writer.add_histogram("test/lin_actions", lin_actions, epoch, max_bins=bins) model.writer.add_histogram("test/ang_actions", ang_actions, epoch, max_bins=bins) counts, bin_edges = np.histogram(lin_actions, bins=bins) fig, ax = plt.subplots() ax.bar( bin_edges[:-1], counts, width=np.diff(bin_edges), align="edge", log=True ) # Log scale on y-axis ax.set_xlabel("Value") ax.set_ylabel("Frequency (Log Scale)") ax.set_title("Histogram with Log Scale") model.writer.add_figure("test/lin_actions_hist", fig) counts, bin_edges = np.histogram(ang_actions, bins=bins) fig, ax = plt.subplots() ax.bar( bin_edges[:-1], counts, width=np.diff(bin_edges), align="edge", log=True ) # Log scale on y-axis ax.set_xlabel("Value") ax.set_ylabel("Frequency (Log Scale)") ax.set_title("Histogram with Log Scale") model.writer.add_figure("test/ang_actions_hist", fig) if __name__ == "__main__": main() ================================================ FILE: robot_nav/marl_test_single.py ================================================ import statistics from pathlib import Path import irsim from tqdm import tqdm from robot_nav.SIM_ENV.sim_env import SIM_ENV from robot_nav.models.MARL.marlTD3.marlTD3 import TD3 import torch import numpy as np class SINGLE_SIM(SIM_ENV): """ Simulation environment for multi-agent robot navigation using IRSim. This class extends the SIM_ENV and provides a wrapper for multi-robot simulation and interaction, supporting reward computation and custom reset logic. Attributes: env (object): IRSim simulation environment instance. robot_goal (np.ndarray): Current goal position(s) for the robots. num_robots (int): Number of robots in the environment. x_range (tuple): World x-range. y_range (tuple): World y-range. """ def __init__( self, world_file="worlds/circle_world.yaml", disable_plotting=False, reward_phase=2, ): """ Initialize the MARL_SIM environment. Args: world_file (str, optional): Path to the world configuration YAML file. disable_plotting (bool, optional): If True, disables IRSim rendering and plotting. """ display = False if disable_plotting else True self.env = irsim.make( world_file, disable_all_plot=disable_plotting, display=display ) self.num_robots = len(self.env.robot_list) self.x_range = self.env._world.x_range self.y_range = self.env._world.y_range self.reward_phase = reward_phase def step(self, action, connection, combined_weights=None): """ Perform a simulation step for all robots using the provided actions and connections. Args: action (list): List of actions for each robot [[lin_vel, ang_vel], ...]. connection (Tensor): Tensor of shape (num_robots, num_robots-1) containing logits indicating connections between robots. combined_weights (Tensor or None, optional): Optional weights for each connection, shape (num_robots, num_robots-1). Returns: tuple: ( poses (list): List of [x, y, theta] for each robot, distances (list): Distance to goal for each robot, coss (list): Cosine of angle to goal for each robot, sins (list): Sine of angle to goal for each robot, collisions (list): Collision status for each robot, goals (list): Goal reached status for each robot, action (list): Actions applied, rewards (list): Rewards computed, positions (list): Current [x, y] for each robot, goal_positions (list): Goal [x, y] for each robot, ) """ act = [ [0, 0] if self.env.robot_list[i].arrive else action[i] for i in range(len(action)) ] self.env.step(action_id=[i for i in range(self.num_robots)], action=act) self.env.render() poses = [] distances = [] coss = [] sins = [] collisions = [] goals = [] rewards = [] positions = [] goal_positions = [] robot_states = [ [self.env.robot_list[i].state[0], self.env.robot_list[i].state[1]] for i in range(self.num_robots) ] for i in range(self.num_robots): robot_state = self.env.robot_list[i].state closest_robots = [ np.linalg.norm( [ robot_states[j][0] - robot_state[0], robot_states[j][1] - robot_state[1], ] ) for j in range(self.num_robots) if j != i ] robot_goal = self.env.robot_list[i].goal goal_vector = [ robot_goal[0].item() - robot_state[0].item(), robot_goal[1].item() - robot_state[1].item(), ] distance = np.linalg.norm(goal_vector) goal = self.env.robot_list[i].arrive pose_vector = [np.cos(robot_state[2]).item(), np.sin(robot_state[2]).item()] cos, sin = self.cossin(pose_vector, goal_vector) collision = self.env.robot_list[i].collision action_i = action[i] reward = self.get_reward( goal, collision, action_i, closest_robots, distance, self.reward_phase ) position = [robot_state[0].item(), robot_state[1].item()] goal_position = [robot_goal[0].item(), robot_goal[1].item()] distances.append(distance) coss.append(cos) sins.append(sin) collisions.append(collision) goals.append(goal) rewards.append(reward) positions.append(position) poses.append( [robot_state[0].item(), robot_state[1].item(), robot_state[2].item()] ) goal_positions.append(goal_position) i_probs = torch.sigmoid( connection[i] ) # connection[i] is logits for "connect" per pair i_connections = i_probs.tolist() i_connections.insert(i, 0) if combined_weights is not None: i_weights = combined_weights[i].tolist() i_weights.insert(i, 0) for j in range(self.num_robots): if i_connections[j] > 0.5: if combined_weights is not None: weight = i_weights[j] else: weight = 1 other_robot_state = self.env.robot_list[j].state other_pos = [ other_robot_state[0].item(), other_robot_state[1].item(), ] rx = [position[0], other_pos[0]] ry = [position[1], other_pos[1]] self.env.draw_trajectory( np.array([rx, ry]), refresh=True, linewidth=weight ) return ( poses, distances, coss, sins, collisions, goals, action, rewards, positions, goal_positions, ) def reset( self, robot_state=None, robot_goal=None, random_obstacles=False, random_obstacle_ids=None, ): """ Reset the simulation environment and optionally set robot and obstacle positions. Args: robot_state (list or None, optional): Initial state for robots as [x, y, theta, speed]. robot_goal (list or None, optional): Goal position(s) for the robots. random_obstacles (bool, optional): If True, randomly position obstacles. random_obstacle_ids (list or None, optional): IDs of obstacles to randomize. Returns: tuple: ( poses (list): List of [x, y, theta] for each robot, distances (list): Distance to goal for each robot, coss (list): Cosine of angle to goal for each robot, sins (list): Sine of angle to goal for each robot, collisions (list): All False after reset, goals (list): All False after reset, action (list): Initial action ([[0.0, 0.0], ...]), rewards (list): Rewards for initial state, positions (list): Initial [x, y] for each robot, goal_positions (list): Initial goal [x, y] for each robot, ) """ self.env.reset() self.robot_goal = self.env.robot.goal action = [[0.0, 0.0] for _ in range(self.num_robots)] con = torch.tensor( [[0.0 for _ in range(self.num_robots - 1)] for _ in range(self.num_robots)] ) poses, distance, cos, sin, _, _, action, reward, positions, goal_positions = ( self.step(action, con) ) return ( poses, distance, cos, sin, [False] * self.num_robots, [False] * self.num_robots, action, reward, positions, goal_positions, ) @staticmethod def get_reward(goal, collision, action, closest_robots, distance, phase=2): """ Calculate the reward for a robot given the current state and action. Args: goal (bool): Whether the robot reached its goal. collision (bool): Whether a collision occurred. action (list): [linear_velocity, angular_velocity] applied. closest_robots (list): Distances to the closest other robots. distance (float): Distance to the goal. phase (int, optional): Reward phase/function selector (default: 1). Returns: float: Computed reward. """ match phase: case 1: if goal: return 100.0 elif collision: return -100.0 * 3 * action[0] else: r_dist = 1.5 / distance cl_pen = 0 for rob in closest_robots: add = 1.5 - rob if rob < 1.5 else 0 cl_pen += add return action[0] - 0.5 * abs(action[1]) - cl_pen + r_dist case 2: if goal: return 70.0 elif collision: return -100.0 * 3 * action[0] else: cl_pen = 0 for rob in closest_robots: add = (3 - rob) ** 2 if rob < 3 else 0 cl_pen += add return -0.5 * abs(action[1]) - cl_pen case _: raise ValueError("Unknown reward phase") def outside_of_bounds(poses): """ Check if any robot is outside the defined world boundaries. Args: poses (list): List of [x, y, theta] poses for each robot. Returns: bool: True if any robot is outside the 21x21 area centered at (6, 6), else False. """ outside = False for pose in poses: norm_x = pose[0] - 6 norm_y = pose[1] - 6 if abs(norm_x) > 10.5 or abs(norm_y) > 10.5: outside = True break return outside def main(args=None): """Main training function""" # ---- Hyperparameters and setup ---- action_dim = 2 # number of actions produced by the model max_action = 1 # maximum absolute value of output actions state_dim = 11 # number of input values in the neural network (vector length of state input) device = torch.device( "cuda" if torch.cuda.is_available() else "cpu" ) # using cuda if it is available, cpu otherwise epoch = 1 # starting epoch number episode = 0 max_steps = 600 # maximum number of steps in single episode steps = 0 # starting step number save_every = 5 # save the model every n training cycles test_scenarios = 100 # ---- Instantiate simulation environment and model ---- sim = SINGLE_SIM( world_file="worlds/circle_world.yaml", disable_plotting=True, reward_phase=2 ) # instantiate environment model = TD3( state_dim=state_dim, action_dim=action_dim, max_action=max_action, num_robots=sim.num_robots, device=device, save_every=save_every, load_model=True, model_name="TDR-MARL-test", load_model_name="TDR-MARL-train", load_directory=Path("robot_nav/models/MARL/marlTD3/checkpoint"), attention="igs", ) # instantiate a model connections = torch.tensor( [[0.0 for _ in range(sim.num_robots - 1)] for _ in range(sim.num_robots)] ) # ---- Take initial step in environment ---- poses, distance, cos, sin, collision, goal, a, reward, positions, goal_positions = ( sim.step([[0, 0] for _ in range(sim.num_robots)], connections) ) # get the initial step state running_goals = 0 running_reward = 0 running_collisions = 0 running_timesteps = 0 ran_out_of_time = 0 reward_per_ep = [] goals_per_ep = [] col_per_ep = [] lin_actions = [] ang_actions = [] entropy_list = [] timesteps_per_ep = [] epsilon = 1e-6 pbar = tqdm(total=test_scenarios) while episode < test_scenarios: state, terminal = model.prepare_state( poses, distance, cos, sin, collision, a, goal_positions ) action, connection, combined_weights = model.get_action( np.array(state), False ) # get an action from the model combined_weights_norm = combined_weights / ( combined_weights.sum(dim=-1, keepdim=True) + epsilon ) entropy = ( ( -(combined_weights_norm * (combined_weights_norm + epsilon).log()) .sum(dim=-1) .mean() ) .data.cpu() .numpy() ) entropy_list.append(entropy) a_in = [[(a[0] + 1) / 4, a[1]] for a in action] ( poses, distance, cos, sin, collision, goal, a, reward, positions, goal_positions, ) = sim.step( a_in, connection, combined_weights ) # get data from the environment running_goals += sum(goal) running_collisions += sum(collision) running_reward += sum(reward) running_timesteps += 1 for j in range(len(a_in)): lin_actions.append(a_in[j][0].item()) ang_actions.append(a_in[j][1].item()) outside = outside_of_bounds(poses) if ( sum(collision) > 0.5 or steps == max_steps or int(sum(goal)) == len(goal) ): # reset environment of terminal state reached, or max_steps were taken ( poses, distance, cos, sin, collision, goal, a, reward, positions, goal_positions, ) = sim.reset() reward_per_ep.append(running_reward) running_reward = 0 goals_per_ep.append(running_goals) running_goals = 0 col_per_ep.append(running_collisions) running_collisions = 0 timesteps_per_ep.append(running_timesteps) running_timesteps = 0 if steps == max_steps: ran_out_of_time += 1 steps = 0 episode += 1 pbar.update(1) else: steps += 1 cols = sum(col_per_ep) / 2 goals_per_ep = np.array(goals_per_ep, dtype=np.float32) col_per_ep = np.array(col_per_ep, dtype=np.float32) avg_ep_col = statistics.mean(col_per_ep) avg_ep_col_std = statistics.stdev(col_per_ep) avg_ep_goals = statistics.mean(goals_per_ep) avg_ep_goals_std = statistics.stdev(goals_per_ep) t_per_ep = np.array(timesteps_per_ep, dtype=np.float32) avg_ep_t = statistics.mean(t_per_ep) avg_ep_t_std = statistics.stdev(t_per_ep) print(f"avg_ep_col: {avg_ep_col}") print(f"avg_ep_col_std: {avg_ep_col_std}") print(f"success rate: {test_scenarios - cols}") print(f"avg_ep_goals: {avg_ep_goals}") print(f"avg_ep_goals_std: {avg_ep_goals_std}") print(f"avg_ep_t: {avg_ep_t}") print(f"avg_ep_t_std: {avg_ep_t_std}") print(f"ran out of time: {ran_out_of_time}") print("..............................................") if __name__ == "__main__": main() ================================================ FILE: robot_nav/marl_train.py ================================================ from pathlib import Path from robot_nav.models.MARL.marlTD3.marlTD3 import TD3 import torch import numpy as np from robot_nav.SIM_ENV.marl_sim import MARL_SIM from utils import get_buffer def outside_of_bounds(poses): """ Check if any robot is outside the defined world boundaries. Args: poses (list): List of [x, y, theta] poses for each robot. Returns: bool: True if any robot is outside the 21x21 area centered at (6, 6), else False. """ outside = False for pose in poses: norm_x = pose[0] - 6 norm_y = pose[1] - 6 if abs(norm_x) > 10.5 or abs(norm_y) > 10.5: outside = True break return outside def main(args=None): """Main training function""" # ---- Hyperparameters and setup ---- action_dim = 2 # number of actions produced by the model max_action = 1 # maximum absolute value of output actions state_dim = 11 # number of input values in the neural network (vector length of state input) device = torch.device( "cuda" if torch.cuda.is_available() else "cpu" ) # using cuda if it is available, cpu otherwise max_epochs = 160 # max number of epochs epoch = 1 # starting epoch number episode = 0 # starting episode number train_every_n = 10 # train and update network parameters every n episodes training_iterations = 80 # how many batches to use for single training cycle batch_size = 16 # batch size for each training iteration max_steps = 300 # maximum number of steps in single episode steps = 0 # starting step number load_saved_buffer = False # whether to load experiences from assets/data.yml pretrain = False # whether to use the loaded experiences to pre-train the model (load_saved_buffer must be True) pretraining_iterations = ( 10 # number of training iterations to run during pre-training ) save_every = 5 # save the model every n training cycles # ---- Instantiate simulation environment and model ---- sim = MARL_SIM( world_file="worlds/multi_robot_world.yaml", disable_plotting=True, reward_phase=1, ) # instantiate environment model = TD3( state_dim=state_dim, action_dim=action_dim, max_action=max_action, num_robots=sim.num_robots, device=device, save_every=save_every, load_model=False, model_name="TDR-MARL-train", load_model_name="saved_model", load_directory=Path("robot_nav/models/MARL/marlTD3/checkpoint"), attention="iga", ) # instantiate a model # ---- Setup replay buffer and initial connections ---- replay_buffer = get_buffer( model, sim, load_saved_buffer, pretrain, pretraining_iterations, training_iterations, batch_size, ) connections = torch.tensor( [[0.0 for _ in range(sim.num_robots - 1)] for _ in range(sim.num_robots)] ) # ---- Take initial step in environment ---- poses, distance, cos, sin, collision, goal, a, reward, positions, goal_positions = ( sim.step([[0, 0] for _ in range(sim.num_robots)], connections) ) # get the initial step state running_goals = 0 running_collisions = 0 running_timesteps = 0 # ---- Main training loop ---- while epoch < max_epochs: # train until max_epochs is reached state, terminal = model.prepare_state( poses, distance, cos, sin, collision, a, goal_positions ) # get state a state representation from returned data from the environment action, connection, combined_weights = model.get_action( np.array(state), True ) # get an action from the model a_in = [ [(a[0] + 1) / 4, a[1]] for a in action ] # clip linear velocity to [0, 0.5] m/s range ( poses, distance, cos, sin, collision, goal, a, reward, positions, goal_positions, ) = sim.step( a_in, connection, combined_weights ) # get data from the environment running_goals += sum(goal) running_collisions += sum(collision) running_timesteps += 1 next_state, terminal = model.prepare_state( poses, distance, cos, sin, collision, a, goal_positions ) # get a next state representation replay_buffer.add( state, action, reward, terminal, next_state ) # add experience to the replay buffer outside = outside_of_bounds(poses) if ( any(terminal) or steps == max_steps or outside ): # reset environment of terminal state reached, or max_steps were taken ( poses, distance, cos, sin, collision, goal, a, reward, positions, goal_positions, ) = sim.reset() episode += 1 if episode % train_every_n == 0: model.writer.add_scalar( "run/avg_goal", running_goals / running_timesteps, epoch ) model.writer.add_scalar( "run/avg_collision", running_collisions / running_timesteps, epoch ) running_goals = 0 running_collisions = 0 running_timesteps = 0 epoch += 1 model.train( replay_buffer=replay_buffer, iterations=training_iterations, batch_size=batch_size, ) # train the model and update its parameters steps = 0 else: steps += 1 if __name__ == "__main__": main() ================================================ FILE: robot_nav/models/CNNTD3/CNNTD3.py ================================================ from pathlib import Path import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from numpy import inf from torch.utils.tensorboard import SummaryWriter from robot_nav.utils import get_max_bound class Actor(nn.Module): """ Actor network for the CNNTD3 agent. This network takes as input a state composed of laser scan data, goal position encoding, and previous action. It processes the scan through a 1D CNN stack and embeds the other inputs before merging all features through fully connected layers to output a continuous action vector. Args: action_dim (int): The dimension of the action space. Architecture: - 1D CNN layers process the laser scan data. - Fully connected layers embed the goal vector (cos, sin, distance) and last action. - Combined features are passed through two fully connected layers with LeakyReLU. - Final action output is scaled with Tanh to bound the values. """ def __init__(self, action_dim): super(Actor, self).__init__() self.cnn1 = nn.Conv1d(1, 4, kernel_size=8, stride=4) self.cnn2 = nn.Conv1d(4, 8, kernel_size=8, stride=4) self.cnn3 = nn.Conv1d(8, 4, kernel_size=4, stride=2) self.goal_embed = nn.Linear(3, 10) self.action_embed = nn.Linear(2, 10) self.layer_1 = nn.Linear(36, 400) torch.nn.init.kaiming_uniform_(self.layer_1.weight, nonlinearity="leaky_relu") self.layer_2 = nn.Linear(400, 300) torch.nn.init.kaiming_uniform_(self.layer_2.weight, nonlinearity="leaky_relu") self.layer_3 = nn.Linear(300, action_dim) self.tanh = nn.Tanh() def forward(self, s): """ Forward pass through the Actor network. Args: s (torch.Tensor): Input state tensor of shape (batch_size, state_dim). The last 5 elements are [distance, cos, sin, lin_vel, ang_vel]. Returns: (torch.Tensor): Action tensor of shape (batch_size, action_dim), with values in range [-1, 1] due to tanh activation. """ if len(s.shape) == 1: s = s.unsqueeze(0) laser = s[:, :-5] goal = s[:, -5:-2] act = s[:, -2:] laser = laser.unsqueeze(1) l = F.leaky_relu(self.cnn1(laser)) l = F.leaky_relu(self.cnn2(l)) l = F.leaky_relu(self.cnn3(l)) l = l.flatten(start_dim=1) g = F.leaky_relu(self.goal_embed(goal)) a = F.leaky_relu(self.action_embed(act)) s = torch.concat((l, g, a), dim=-1) s = F.leaky_relu(self.layer_1(s)) s = F.leaky_relu(self.layer_2(s)) a = self.tanh(self.layer_3(s)) return a class Critic(nn.Module): """ Critic network for the CNNTD3 agent. The Critic estimates Q-values for state-action pairs using two separate sub-networks (Q1 and Q2), as required by the TD3 algorithm. Each sub-network uses a combination of CNN-extracted features, embedded goal and previous action features, and the current action. Args: action_dim (int): The dimension of the action space. Architecture: - Shared CNN layers process the laser scan input. - Goal and previous action are embedded and concatenated. - Each Q-network uses separate fully connected layers to produce scalar Q-values. - Both Q-networks receive the full state and current action. - Outputs two Q-value tensors (Q1, Q2) for TD3-style training and target smoothing. """ def __init__(self, action_dim): super(Critic, self).__init__() self.cnn1 = nn.Conv1d(1, 4, kernel_size=8, stride=4) self.cnn2 = nn.Conv1d(4, 8, kernel_size=8, stride=4) self.cnn3 = nn.Conv1d(8, 4, kernel_size=4, stride=2) self.goal_embed = nn.Linear(3, 10) self.action_embed = nn.Linear(2, 10) self.layer_1 = nn.Linear(36, 400) torch.nn.init.kaiming_uniform_(self.layer_1.weight, nonlinearity="leaky_relu") self.layer_2_s = nn.Linear(400, 300) torch.nn.init.kaiming_uniform_(self.layer_2_s.weight, nonlinearity="leaky_relu") self.layer_2_a = nn.Linear(action_dim, 300) torch.nn.init.kaiming_uniform_(self.layer_2_a.weight, nonlinearity="leaky_relu") self.layer_3 = nn.Linear(300, 1) torch.nn.init.kaiming_uniform_(self.layer_3.weight, nonlinearity="leaky_relu") self.layer_4 = nn.Linear(36, 400) torch.nn.init.kaiming_uniform_(self.layer_1.weight, nonlinearity="leaky_relu") self.layer_5_s = nn.Linear(400, 300) torch.nn.init.kaiming_uniform_(self.layer_5_s.weight, nonlinearity="leaky_relu") self.layer_5_a = nn.Linear(action_dim, 300) torch.nn.init.kaiming_uniform_(self.layer_5_a.weight, nonlinearity="leaky_relu") self.layer_6 = nn.Linear(300, 1) torch.nn.init.kaiming_uniform_(self.layer_6.weight, nonlinearity="leaky_relu") def forward(self, s, action): """ Forward pass through both Q-networks of the Critic. Args: s (torch.Tensor): Input state tensor of shape (batch_size, state_dim). The last 5 elements are [distance, cos, sin, lin_vel, ang_vel]. action (torch.Tensor): Current action tensor of shape (batch_size, action_dim). Returns: (tuple): - q1 (torch.Tensor): First Q-value estimate (batch_size, 1). - q2 (torch.Tensor): Second Q-value estimate (batch_size, 1). """ laser = s[:, :-5] goal = s[:, -5:-2] act = s[:, -2:] laser = laser.unsqueeze(1) l = F.leaky_relu(self.cnn1(laser)) l = F.leaky_relu(self.cnn2(l)) l = F.leaky_relu(self.cnn3(l)) l = l.flatten(start_dim=1) g = F.leaky_relu(self.goal_embed(goal)) a = F.leaky_relu(self.action_embed(act)) s = torch.concat((l, g, a), dim=-1) s1 = F.leaky_relu(self.layer_1(s)) self.layer_2_s(s1) self.layer_2_a(action) s11 = torch.mm(s1, self.layer_2_s.weight.data.t()) s12 = torch.mm(action, self.layer_2_a.weight.data.t()) s1 = F.leaky_relu(s11 + s12 + self.layer_2_a.bias.data) q1 = self.layer_3(s1) s2 = F.leaky_relu(self.layer_4(s)) self.layer_5_s(s2) self.layer_5_a(action) s21 = torch.mm(s2, self.layer_5_s.weight.data.t()) s22 = torch.mm(action, self.layer_5_a.weight.data.t()) s2 = F.leaky_relu(s21 + s22 + self.layer_5_a.bias.data) q2 = self.layer_6(s2) return q1, q2 # CNNTD3 network class CNNTD3(object): """ CNNTD3 (Twin Delayed Deep Deterministic Policy Gradient with CNN-based inputs) agent for continuous control tasks. This class encapsulates the full implementation of the TD3 algorithm using neural network architectures for the actor and critic, with optional bounding for critic outputs to regularize learning. The agent is designed to train in environments where sensor observations (e.g., LiDAR) are used for navigation tasks. Args: state_dim (int): Dimension of the input state. action_dim (int): Dimension of the output action. max_action (float): Maximum magnitude of the action. device (torch.device): Torch device to use (CPU or GPU). lr (float): Learning rate for both actor and critic optimizers. save_every (int): Save model every N training iterations (0 to disable). load_model (bool): Whether to load a pre-trained model at initialization. save_directory (Path): Path to the directory for saving model checkpoints. model_name (str): Base name for the saved model files. load_directory (Path): Path to load model checkpoints from (if `load_model=True`). use_max_bound (bool): Whether to apply maximum Q-value bounding during training. bound_weight (float): Weight for the bounding loss term in total loss. """ def __init__( self, state_dim, action_dim, max_action, device, lr=1e-4, save_every=0, load_model=False, save_directory=Path("robot_nav/models/CNNTD3/checkpoint"), model_name="CNNTD3", load_directory=Path("robot_nav/models/CNNTD3/checkpoint"), use_max_bound=False, bound_weight=0.25, ): # Initialize the Actor network self.device = device self.actor = Actor(action_dim).to(self.device) self.actor_target = Actor(action_dim).to(self.device) self.actor_target.load_state_dict(self.actor.state_dict()) self.actor_optimizer = torch.optim.Adam(params=self.actor.parameters(), lr=lr) # Initialize the Critic networks self.critic = Critic(action_dim).to(self.device) self.critic_target = Critic(action_dim).to(self.device) self.critic_target.load_state_dict(self.critic.state_dict()) self.critic_optimizer = torch.optim.Adam(params=self.critic.parameters(), lr=lr) self.action_dim = action_dim self.max_action = max_action self.state_dim = state_dim self.writer = SummaryWriter(comment=model_name) self.iter_count = 0 if load_model: self.load(filename=model_name, directory=load_directory) self.save_every = save_every self.model_name = model_name self.save_directory = save_directory self.use_max_bound = use_max_bound self.bound_weight = bound_weight def get_action(self, obs, add_noise): """ Selects an action for a given observation. Args: obs (np.ndarray): The current observation/state. add_noise (bool): Whether to add exploration noise to the action. Returns: (np.ndarray): The selected action. """ if add_noise: return ( self.act(obs) + np.random.normal(0, 0.2, size=self.action_dim) ).clip(-self.max_action, self.max_action) else: return self.act(obs) def act(self, state): """ Computes the deterministic action from the actor network for a given state. Args: state (np.ndarray): Input state. Returns: (np.ndarray): Action predicted by the actor network. """ # Function to get the action from the actor state = torch.Tensor(state).to(self.device) return self.actor(state).cpu().data.numpy().flatten() # training cycle def train( self, replay_buffer, iterations, batch_size, discount=0.99, tau=0.005, policy_noise=0.2, noise_clip=0.5, policy_freq=2, max_lin_vel=0.5, max_ang_vel=1, goal_reward=100, distance_norm=10, time_step=0.3, ): """ Trains the CNNTD3 agent using sampled batches from the replay buffer. Args: replay_buffer (ReplayBuffer): Buffer storing environment transitions. iterations (int): Number of training iterations. batch_size (int): Size of each training batch. discount (float): Discount factor for future rewards. tau (float): Soft update rate for target networks. policy_noise (float): Std. dev. of noise added to target policy. noise_clip (float): Maximum value for target policy noise. policy_freq (int): Frequency of actor and target network updates. max_lin_vel (float): Maximum linear velocity for bounding calculations. max_ang_vel (float): Maximum angular velocity for bounding calculations. goal_reward (float): Reward value for reaching the goal. distance_norm (float): Normalization factor for distance in bounding. time_step (float): Time delta between steps. """ av_Q = 0 max_Q = -inf av_loss = 0 for it in range(iterations): # sample a batch from the replay buffer ( batch_states, batch_actions, batch_rewards, batch_dones, batch_next_states, ) = replay_buffer.sample_batch(batch_size) state = torch.Tensor(batch_states).to(self.device) next_state = torch.Tensor(batch_next_states).to(self.device) action = torch.Tensor(batch_actions).to(self.device) reward = torch.Tensor(batch_rewards).to(self.device).reshape(-1, 1) done = torch.Tensor(batch_dones).to(self.device).reshape(-1, 1) # Obtain the estimated action from the next state by using the actor-target next_action = self.actor_target(next_state) # Add noise to the action noise = ( torch.Tensor(batch_actions) .data.normal_(0, policy_noise) .to(self.device) ) noise = noise.clamp(-noise_clip, noise_clip) next_action = (next_action + noise).clamp(-self.max_action, self.max_action) # Calculate the Q values from the critic-target network for the next state-action pair target_Q1, target_Q2 = self.critic_target(next_state, next_action) # Select the minimal Q value from the 2 calculated values target_Q = torch.min(target_Q1, target_Q2) av_Q += torch.mean(target_Q) max_Q = max(max_Q, torch.max(target_Q)) # Calculate the final Q value from the target network parameters by using Bellman equation target_Q = reward + ((1 - done) * discount * target_Q).detach() # Get the Q values of the basis networks with the current parameters current_Q1, current_Q2 = self.critic(state, action) # Calculate the loss between the current Q value and the target Q value loss = F.mse_loss(current_Q1, target_Q) + F.mse_loss(current_Q2, target_Q) if self.use_max_bound: max_bound = get_max_bound( next_state, discount, max_ang_vel, max_lin_vel, time_step, distance_norm, goal_reward, reward, done, self.device, ) max_excess_Q1 = F.relu(current_Q1 - max_bound) max_excess_Q2 = F.relu(current_Q2 - max_bound) max_bound_loss = (max_excess_Q1**2).mean() + (max_excess_Q2**2).mean() # Add loss for Q values exceeding maximum possible upper bound loss += self.bound_weight * max_bound_loss # Perform the gradient descent self.critic_optimizer.zero_grad() loss.backward() self.critic_optimizer.step() if it % policy_freq == 0: # Maximize the actor output value by performing gradient descent on negative Q values # (essentially perform gradient ascent) actor_grad, _ = self.critic(state, self.actor(state)) actor_grad = -actor_grad.mean() self.actor_optimizer.zero_grad() actor_grad.backward() self.actor_optimizer.step() # Use soft update to update the actor-target network parameters by # infusing small amount of current parameters for param, target_param in zip( self.actor.parameters(), self.actor_target.parameters() ): target_param.data.copy_( tau * param.data + (1 - tau) * target_param.data ) # Use soft update to update the critic-target network parameters by infusing # small amount of current parameters for param, target_param in zip( self.critic.parameters(), self.critic_target.parameters() ): target_param.data.copy_( tau * param.data + (1 - tau) * target_param.data ) av_loss += loss self.iter_count += 1 # Write new values for tensorboard self.writer.add_scalar("train/loss", av_loss / iterations, self.iter_count) self.writer.add_scalar("train/avg_Q", av_Q / iterations, self.iter_count) self.writer.add_scalar("train/max_Q", max_Q, self.iter_count) if self.save_every > 0 and self.iter_count % self.save_every == 0: self.save(filename=self.model_name, directory=self.save_directory) def save(self, filename, directory): """ Saves the current model parameters to the specified directory. Args: filename (str): Base filename for saved files. directory (Path): Path to save the model files. """ Path(directory).mkdir(parents=True, exist_ok=True) torch.save(self.actor.state_dict(), "%s/%s_actor.pth" % (directory, filename)) torch.save( self.actor_target.state_dict(), "%s/%s_actor_target.pth" % (directory, filename), ) torch.save(self.critic.state_dict(), "%s/%s_critic.pth" % (directory, filename)) torch.save( self.critic_target.state_dict(), "%s/%s_critic_target.pth" % (directory, filename), ) def load(self, filename, directory): """ Loads model parameters from the specified directory. Args: filename (str): Base filename for saved files. directory (Path): Path to load the model files from. """ self.actor.load_state_dict( torch.load("%s/%s_actor.pth" % (directory, filename)) ) self.actor_target.load_state_dict( torch.load("%s/%s_actor_target.pth" % (directory, filename)) ) self.critic.load_state_dict( torch.load("%s/%s_critic.pth" % (directory, filename)) ) self.critic_target.load_state_dict( torch.load("%s/%s_critic_target.pth" % (directory, filename)) ) print(f"Loaded weights from: {directory}") def prepare_state(self, latest_scan, distance, cos, sin, collision, goal, action): """ Prepares the environment's raw sensor data and navigation variables into a format suitable for learning. Args: latest_scan (list or np.ndarray): Raw scan data (e.g., LiDAR). distance (float): Distance to goal. cos (float): Cosine of heading angle to goal. sin (float): Sine of heading angle to goal. collision (bool): Collision status (True if collided). goal (bool): Goal reached status. action (list or np.ndarray): Last action taken [lin_vel, ang_vel]. Returns: (tuple): - state (list): Normalized and concatenated state vector. - terminal (int): Terminal flag (1 if collision or goal, else 0). """ latest_scan = np.array(latest_scan) inf_mask = np.isinf(latest_scan) latest_scan[inf_mask] = 7.0 latest_scan /= 7 # Normalize to [0, 1] range distance /= 10 lin_vel = action[0] * 2 ang_vel = (action[1] + 1) / 2 state = latest_scan.tolist() + [distance, cos, sin] + [lin_vel, ang_vel] assert len(state) == self.state_dim terminal = 1 if collision or goal else 0 return state, terminal ================================================ FILE: robot_nav/models/CNNTD3/__init__.py ================================================ ================================================ FILE: robot_nav/models/DDPG/DDPG.py ================================================ from pathlib import Path import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from numpy import inf from torch.utils.tensorboard import SummaryWriter from robot_nav.utils import get_max_bound class Actor(nn.Module): """ Actor network for the DDPG algorithm. This network maps input states to actions using a fully connected feedforward architecture. It uses Leaky ReLU activations in the hidden layers and a tanh activation at the output to ensure the output actions are in the range [-1, 1]. Architecture: - Linear(state_dim → 400) + LeakyReLU - Linear(400 → 300) + LeakyReLU - Linear(300 → action_dim) + Tanh Args: state_dim (int): Dimension of the input state. action_dim (int): Dimension of the output action space. """ def __init__(self, state_dim, action_dim): super(Actor, self).__init__() self.layer_1 = nn.Linear(state_dim, 400) torch.nn.init.kaiming_uniform_(self.layer_1.weight, nonlinearity="leaky_relu") self.layer_2 = nn.Linear(400, 300) torch.nn.init.kaiming_uniform_(self.layer_2.weight, nonlinearity="leaky_relu") self.layer_3 = nn.Linear(300, action_dim) self.tanh = nn.Tanh() def forward(self, s): """ Forward pass of the actor network. Args: s (torch.Tensor): Input state tensor of shape (batch_size, state_dim). Returns: (torch.Tensor): Output action tensor of shape (batch_size, action_dim), scaled to [-1, 1]. """ s = F.leaky_relu(self.layer_1(s)) s = F.leaky_relu(self.layer_2(s)) a = self.tanh(self.layer_3(s)) return a class Critic(nn.Module): """ Critic network for the DDPG algorithm. This network evaluates the Q-value of a given state-action pair. It separately processes state and action inputs through linear layers, combines them, and passes the result through another linear layer to predict a scalar Q-value. Architecture: - Linear(state_dim → 400) + LeakyReLU - Linear(400 → 300) [state branch] - Linear(action_dim → 300) [action branch] - Combine both branches, apply LeakyReLU - Linear(300 → 1) for Q-value output Args: state_dim (int): Dimension of the input state. action_dim (int): Dimension of the input action. """ def __init__(self, state_dim, action_dim): super(Critic, self).__init__() self.layer_1 = nn.Linear(state_dim, 400) torch.nn.init.kaiming_uniform_(self.layer_1.weight, nonlinearity="leaky_relu") self.layer_2_s = nn.Linear(400, 300) torch.nn.init.kaiming_uniform_(self.layer_2_s.weight, nonlinearity="leaky_relu") self.layer_2_a = nn.Linear(action_dim, 300) torch.nn.init.kaiming_uniform_(self.layer_2_a.weight, nonlinearity="leaky_relu") self.layer_3 = nn.Linear(300, 1) torch.nn.init.kaiming_uniform_(self.layer_3.weight, nonlinearity="leaky_relu") def forward(self, s, a): """ Forward pass of the critic network. Args: s (torch.Tensor): State tensor of shape (batch_size, state_dim). a (torch.Tensor): Action tensor of shape (batch_size, action_dim). Returns: (torch.Tensor): Q-value tensor of shape (batch_size, 1). """ s1 = F.leaky_relu(self.layer_1(s)) self.layer_2_s(s1) self.layer_2_a(a) s11 = torch.mm(s1, self.layer_2_s.weight.data.t()) s12 = torch.mm(a, self.layer_2_a.weight.data.t()) s1 = F.leaky_relu(s11 + s12 + self.layer_2_a.bias.data) q = self.layer_3(s1) return q # DDPG network class DDPG(object): """ Deep Deterministic Policy Gradient (DDPG) agent implementation. This class encapsulates the actor-critic learning framework using DDPG, which is suitable for continuous action spaces. It supports training, action selection, model saving/loading, and state preparation for a reinforcement learning agent, specifically designed for robot navigation. Args: state_dim (int): Dimension of the input state. action_dim (int): Dimension of the action space. max_action (float): Maximum action value allowed. device (torch.device): Computation device (CPU or GPU). lr (float): Learning rate for the optimizers. Default is 1e-4. save_every (int): Frequency of saving the model in training iterations. 0 means no saving. Default is 0. load_model (bool): Flag indicating whether to load a model from disk. Default is False. save_directory (Path): Directory to save the model checkpoints. Default is "robot_nav/models/DDPG/checkpoint". model_name (str): Name used for saving and TensorBoard logging. Default is "DDPG". load_directory (Path): Directory to load model checkpoints from. Default is "robot_nav/models/DDPG/checkpoint". use_max_bound (bool): Whether to enforce a learned upper bound on the Q-value. Default is False. bound_weight (float): Weight of the upper bound loss penalty. Default is 0.25. """ def __init__( self, state_dim, action_dim, max_action, device, lr=1e-4, save_every=0, load_model=False, save_directory=Path("robot_nav/models/DDPG/checkpoint"), model_name="DDPG", load_directory=Path("robot_nav/models/DDPG/checkpoint"), use_max_bound=False, bound_weight=0.25, ): # Initialize the Actor network self.device = device self.actor = Actor(state_dim, action_dim).to(self.device) self.actor_target = Actor(state_dim, action_dim).to(self.device) self.actor_target.load_state_dict(self.actor.state_dict()) self.actor_optimizer = torch.optim.Adam(params=self.actor.parameters(), lr=lr) # Initialize the Critic networks self.critic = Critic(state_dim, action_dim).to(self.device) self.critic_target = Critic(state_dim, action_dim).to(self.device) self.critic_target.load_state_dict(self.critic.state_dict()) self.critic_optimizer = torch.optim.Adam(params=self.critic.parameters(), lr=lr) self.action_dim = action_dim self.max_action = max_action self.state_dim = state_dim self.writer = SummaryWriter(comment=model_name) self.iter_count = 0 if load_model: self.load(filename=model_name, directory=load_directory) self.save_every = save_every self.model_name = model_name self.save_directory = save_directory self.use_max_bound = use_max_bound self.bound_weight = bound_weight def get_action(self, obs, add_noise): """ Selects an action based on the observation. Args: obs (np.array): The current state observation. add_noise (bool): Whether to add exploration noise to the action. Returns: (np.array): Action selected by the actor network. """ if add_noise: return ( self.act(obs) + np.random.normal(0, 0.2, size=self.action_dim) ).clip(-self.max_action, self.max_action) else: return self.act(obs) def act(self, state): """ Computes the action for a given state using the actor network. Args: state (np.array): Environment state. Returns: (np.array): Action values as output by the actor network. """ state = torch.Tensor(state).to(self.device) return self.actor(state).cpu().data.numpy().flatten() # training cycle def train( self, replay_buffer, iterations, batch_size, discount=0.99, tau=0.005, policy_noise=0.2, noise_clip=0.5, policy_freq=2, max_lin_vel=0.5, max_ang_vel=1, goal_reward=100, distance_norm=10, time_step=0.3, ): """ Trains the actor and critic networks using a replay buffer and soft target updates. Args: replay_buffer (ReplayBuffer): Replay buffer object with a sample_batch method. iterations (int): Number of training iterations. batch_size (int): Size of each training batch. discount (float): Discount factor for future rewards. tau (float): Soft update factor for target networks. policy_noise (float): Standard deviation of noise added to target policy. noise_clip (float): Maximum value to clip target policy noise. policy_freq (int): Frequency of actor and target updates. max_lin_vel (float): Maximum linear velocity, used in Q-bound calculation. max_ang_vel (float): Maximum angular velocity, used in Q-bound calculation. goal_reward (float): Reward given upon reaching goal. distance_norm (float): Distance normalization factor. time_step (float): Time step used in max bound calculation. """ av_Q = 0 max_Q = -inf av_loss = 0 for it in range(iterations): # sample a batch from the replay buffer ( batch_states, batch_actions, batch_rewards, batch_dones, batch_next_states, ) = replay_buffer.sample_batch(batch_size) state = torch.Tensor(batch_states).to(self.device) next_state = torch.Tensor(batch_next_states).to(self.device) action = torch.Tensor(batch_actions).to(self.device) reward = torch.Tensor(batch_rewards).to(self.device).reshape(-1, 1) done = torch.Tensor(batch_dones).to(self.device).reshape(-1, 1) # Obtain the estimated action from the next state by using the actor-target next_action = self.actor_target(next_state) # Add noise to the action noise = ( torch.Tensor(batch_actions) .data.normal_(0, policy_noise) .to(self.device) ) noise = noise.clamp(-noise_clip, noise_clip) next_action = (next_action + noise).clamp(-self.max_action, self.max_action) # Calculate the Q values from the critic-target network for the next state-action pair target_Q = self.critic_target(next_state, next_action) av_Q += torch.mean(target_Q) max_Q = max(max_Q, torch.max(target_Q)) # Calculate the final Q value from the target network parameters by using Bellman equation target_Q = reward + ((1 - done) * discount * target_Q).detach() # Get the Q values of the basis networks with the current parameters current_Q = self.critic(state, action) # Calculate the loss between the current Q value and the target Q value loss = F.mse_loss(current_Q, target_Q) if self.use_max_bound: max_bound = get_max_bound( next_state, discount, max_ang_vel, max_lin_vel, time_step, distance_norm, goal_reward, reward, done, self.device, ) max_excess_Q = F.relu(current_Q - max_bound) max_bound_loss = (max_excess_Q**2).mean() # Add loss for Q values exceeding maximum possible upper bound loss += self.bound_weight * max_bound_loss # Perform the gradient descent self.critic_optimizer.zero_grad() loss.backward() self.critic_optimizer.step() if it % policy_freq == 0: # Maximize the actor output value by performing gradient descent on negative Q values # (essentially perform gradient ascent) actor_grad = self.critic(state, self.actor(state)) actor_grad = -actor_grad.mean() self.actor_optimizer.zero_grad() actor_grad.backward() self.actor_optimizer.step() # Use soft update to update the actor-target network parameters by # infusing small amount of current parameters for param, target_param in zip( self.actor.parameters(), self.actor_target.parameters() ): target_param.data.copy_( tau * param.data + (1 - tau) * target_param.data ) # Use soft update to update the critic-target network parameters by infusing # small amount of current parameters for param, target_param in zip( self.critic.parameters(), self.critic_target.parameters() ): target_param.data.copy_( tau * param.data + (1 - tau) * target_param.data ) av_loss += loss self.iter_count += 1 # Write new values for tensorboard self.writer.add_scalar("train/loss", av_loss / iterations, self.iter_count) self.writer.add_scalar("train/avg_Q", av_Q / iterations, self.iter_count) self.writer.add_scalar("train/max_Q", max_Q, self.iter_count) if self.save_every > 0 and self.iter_count % self.save_every == 0: self.save(filename=self.model_name, directory=self.save_directory) def save(self, filename, directory): """ Saves the model parameters to disk. Args: filename (str): Base filename for saving the model components. directory (str or Path): Directory where the model files will be saved. """ Path(directory).mkdir(parents=True, exist_ok=True) torch.save(self.actor.state_dict(), "%s/%s_actor.pth" % (directory, filename)) torch.save( self.actor_target.state_dict(), "%s/%s_actor_target.pth" % (directory, filename), ) torch.save(self.critic.state_dict(), "%s/%s_critic.pth" % (directory, filename)) torch.save( self.critic_target.state_dict(), "%s/%s_critic_target.pth" % (directory, filename), ) def load(self, filename, directory): """ Loads model parameters from disk. Args: filename (str): Base filename used for loading model components. directory (str or Path): Directory to load the model files from. """ self.actor.load_state_dict( torch.load("%s/%s_actor.pth" % (directory, filename)) ) self.actor_target.load_state_dict( torch.load("%s/%s_actor_target.pth" % (directory, filename)) ) self.critic.load_state_dict( torch.load("%s/%s_critic.pth" % (directory, filename)) ) self.critic_target.load_state_dict( torch.load("%s/%s_critic_target.pth" % (directory, filename)) ) print(f"Loaded weights from: {directory}") def prepare_state(self, latest_scan, distance, cos, sin, collision, goal, action): """ Processes raw sensor input and additional information into a normalized state representation. Args: latest_scan (list or np.array): Raw LIDAR or laser scan data. distance (float): Distance to the goal. cos (float): Cosine of the angle to the goal. sin (float): Sine of the angle to the goal. collision (bool): Whether a collision has occurred. goal (bool): Whether the goal has been reached. action (list or np.array): The action taken in the previous step. Returns: (tuple): (state vector, terminal flag) """ latest_scan = np.array(latest_scan) inf_mask = np.isinf(latest_scan) latest_scan[inf_mask] = 7.0 max_bins = self.state_dim - 5 bin_size = int(np.ceil(len(latest_scan) / max_bins)) # Initialize the list to store the minimum values of each bin min_values = [] # Loop through the data and create bins for i in range(0, len(latest_scan), bin_size): # Get the current bin bin = latest_scan[i : i + min(bin_size, len(latest_scan) - i)] # Find the minimum value in the current bin and append it to the min_values list min_values.append(min(bin) / 7) # Normalize to [0, 1] range distance /= 10 lin_vel = action[0] * 2 ang_vel = (action[1] + 1) / 2 state = min_values + [distance, cos, sin] + [lin_vel, ang_vel] assert len(state) == self.state_dim terminal = 1 if collision or goal else 0 return state, terminal ================================================ FILE: robot_nav/models/DDPG/__init__.py ================================================ ================================================ FILE: robot_nav/models/HCM/__init__.py ================================================ ================================================ FILE: robot_nav/models/HCM/hardcoded_model.py ================================================ from math import atan2 import numpy as np from numpy import clip from torch.utils.tensorboard import SummaryWriter import yaml class HCM(object): """ A class representing a Hard-Coded model (HCM) for a robot's navigation system. This class contains methods for generating actions based on the robot's state, preparing state representations, training (placeholder method), saving/loading models, and logging experiences. The method is suboptimal in order to collect collisions for pre-training of DRL models. Attributes: max_action (float): The maximum possible action value. state_dim (int): The dimension of the state representation. writer (SummaryWriter): The writer for logging purposes. iterator (int): A counter for tracking sample addition. save_samples (bool): Whether to save the samples to a file. max_added_samples (int): Maximum number of samples to be added to the saved file. file_location (str): The file location for saving samples. """ def __init__( self, state_dim, max_action, save_samples, max_added_samples=10_000, file_location="robot_nav/assets/data.yml", ): """ Initialize the HCM class with the provided configuration. Args: state_dim (int): The dimension of the state space. max_action (float): The maximum possible action value. save_samples (bool): Whether to save samples to a file. max_added_samples (int): The maximum number of samples to save. file_location (str): The file path for saving samples. """ self.max_action = max_action self.state_dim = state_dim self.writer = SummaryWriter() self.iterator = 0 self.save_samples = save_samples self.max_added_samples = max_added_samples self.file_location = file_location def get_action(self, state, add_noise): """ Compute the action to be taken based on the current state of the robot. Args: state (list): The current state of the robot, including LIDAR scan, distance, and other relevant features. add_noise (bool): Whether to add noise to the action for exploration. Returns: (list): The computed action [linear velocity, angular velocity]. """ sin = state[-3] cos = state[-4] angle = atan2(sin, cos) laser_nr = self.state_dim - 5 limit = 1.5 if min(state[4 : self.state_dim - 9]) < limit: state = state.tolist() idx = state[:laser_nr].index(min(state[:laser_nr])) if idx > laser_nr / 2: sign = -1 else: sign = 1 idx = clip(idx + sign * 5 * (limit / min(state[:laser_nr])), 0, laser_nr) angle = ((3.14 / (laser_nr)) * idx) - 1.57 rot_vel = clip(angle, -1.0, 1.0) lin_vel = -abs(rot_vel / 2) return [lin_vel, rot_vel] # training cycle def train( self, replay_buffer, iterations, batch_size, discount=0.99999, tau=0.005, policy_noise=0.2, noise_clip=0.5, policy_freq=2, ): """ Placeholder method for training the hybrid control model. Args: replay_buffer (object): The replay buffer containing experiences. iterations (int): The number of training iterations. batch_size (int): The batch size for training. discount (float): The discount factor for future rewards. tau (float): The soft update parameter for target networks. policy_noise (float): The noise added to actions during training. noise_clip (float): The clipping value for action noise. policy_freq (int): The frequency at which to update the policy. Note: This method is a placeholder and currently does nothing. """ pass def save(self, filename, directory): """ Placeholder method to save the current model state to a file. Args: filename (str): The name of the file where the model will be saved. directory (str): The directory where the file will be stored. Note: This method is a placeholder and currently does nothing. """ pass def load(self, filename, directory): """ Placeholder method to load a model state from a file. Args: filename (str): The name of the file to load the model from. directory (str): The directory where the model file is stored. Note: This method is a placeholder and currently does nothing. """ pass def prepare_state(self, latest_scan, distance, cos, sin, collision, goal, action): """ Prepare the state representation for the model based on the current environment. Args: latest_scan (list): The LIDAR scan data. distance (float): The distance to the goal. cos (float): The cosine of the robot's orientation angle. sin (float): The sine of the robot's orientation angle. collision (bool): Whether a collision occurred. goal (bool): Whether the goal has been reached. action (list): The action taken by the robot, [linear velocity, angular velocity]. Returns: (tuple): A tuple containing the prepared state and a terminal flag (1 if terminal state, 0 otherwise). """ latest_scan = np.array(latest_scan) inf_mask = np.isinf(latest_scan) latest_scan[inf_mask] = 7.0 max_bins = self.state_dim - 5 bin_size = int(np.ceil(len(latest_scan) / max_bins)) # Initialize the list to store the minimum values of each bin min_values = [] # Loop through the data and create bins for i in range(0, len(latest_scan), bin_size): # Get the current bin bin = latest_scan[i : i + min(bin_size, len(latest_scan) - i)] # Find the minimum value in the current bin and append it to the min_values list min_values.append(min(bin)) state = min_values + [distance, cos, sin] + [action[0], action[1]] assert len(state) == self.state_dim terminal = 1 if collision or goal else 0 self.iterator += 1 if self.save_samples and self.iterator < self.max_added_samples: action = action if type(action) is list else action action = [float(a) for a in action] sample = { self.iterator: { "latest_scan": latest_scan.tolist(), "distance": distance.tolist(), "cos": cos.tolist(), "sin": sin.tolist(), "collision": collision, "goal": goal, "action": action, } } with open(self.file_location, "a") as outfile: yaml.dump(sample, outfile, default_flow_style=False) return state, terminal ================================================ FILE: robot_nav/models/MARL/Attention/__init__.py ================================================ ================================================ FILE: robot_nav/models/MARL/Attention/g2anet.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F import numpy as np class G2ANet(nn.Module): """ Graph-based attention network for multi-agent coordination. Encodes per-agent features, builds pairwise geometric edge features, computes hard (binary) attention and soft (weighted) attention, and decodes the concatenation of self and attended features. Args: embedding_dim (int): Dimensionality of the internal agent embedding. Attributes: hard_encoding (nn.Linear): Outputs logits for binary attention (2 classes). q (nn.Linear): Query projection for soft attention. k (nn.Linear): Key projection from edge features for soft attention. v (nn.Linear): Value projection from edge features for soft attention. hard_mlp (nn.Sequential): MLP over concatenated node/edge features for hard attention. decoding (nn.Linear): Final decoder mapping concatenated embeddings to output. embedding1 (nn.Linear): First layer for agent feature encoding. embedding2 (nn.Linear): Second layer for agent feature encoding. """ def __init__(self, embedding_dim): super(G2ANet, self).__init__() self.embedding_dim = embedding_dim self.hard_encoding = nn.Linear(embedding_dim, 2) # Soft self.q = nn.Linear(embedding_dim, embedding_dim, bias=False) self.k = nn.Linear(10, embedding_dim, bias=False) self.v = nn.Linear(10, embedding_dim) self.hard_mlp = nn.Sequential( nn.Linear(embedding_dim + 7, embedding_dim), nn.ReLU(), nn.Linear(embedding_dim, embedding_dim), ) self.decoding = nn.Linear(embedding_dim * 2, embedding_dim * 2) self.embedding1 = nn.Linear(5, 128) nn.init.kaiming_uniform_(self.embedding1.weight, nonlinearity="leaky_relu") self.embedding2 = nn.Linear(128, embedding_dim) nn.init.kaiming_uniform_(self.embedding2.weight, nonlinearity="leaky_relu") def encode_agent_features(self, embed): """ Encode per-agent features with a two-layer MLP. Args: embed (Tensor): Raw agent features of shape (B*N, 5). Returns: Tensor: Encoded embeddings of shape (B*N, embedding_dim). """ embed = F.leaky_relu(self.embedding1(embed)) embed = F.leaky_relu(self.embedding2(embed)) return embed def forward(self, embedding): """ Compute hard and soft attentions and produce attended embeddings. Args: embedding (Tensor): Input tensor of shape (B, N, D), where D ≥ 11. Expected to include position (x,y), heading (cos,sin), agent features (for encoding), action, and goal. Returns: tuple: output (Tensor): Attended embedding, shape (B*N, 2*embedding_dim). hard_logits (Tensor): Hard attention logits (keep class index 1), shape (B*N, N-1). unnorm_rel_dist (Tensor): Unnormalized pairwise distances, shape (B*N, N-1, 1). mean_entropy (Tensor): Scalar mean entropy of combined (soft×hard) weights. hard_weights (Tensor): Binary hard attention mask, shape (B, N, N-1). comb_w (Tensor): Combined weights per (receiver, sender) after soft×hard, shape (N, N*(N-1)). """ if embedding.dim() == 2: embedding = embedding.unsqueeze(0) batch_size, n_agents, _ = embedding.shape # Extract sub-features embed = embedding[:, :, 4:9].reshape(batch_size * n_agents, -1) position = embedding[:, :, :2].reshape(batch_size, n_agents, 2) heading = embedding[:, :, 2:4].reshape( batch_size, n_agents, 2 ) # assume (cos(θ), sin(θ)) action = embedding[:, :, 7:9].reshape(batch_size, n_agents, 2) goal = embedding[:, :, -2:].reshape(batch_size, n_agents, 2) # Compute pairwise relative goal vectors (for each i,j) goal_j = goal.unsqueeze(1).expand(-1, n_agents, -1, -1) pos_i = position.unsqueeze(2) goal_rel_vec = goal_j - pos_i # Encode agent features agent_embed = self.encode_agent_features(embed) agent_embed = agent_embed.view(batch_size, n_agents, self.embedding_dim) # Prep for hard attention: compute all relative geometry for each agent pair h_i = agent_embed.unsqueeze(2) # (B, N, 1, D) pos_i = position.unsqueeze(2) # (B, N, 1, 2) pos_j = position.unsqueeze(1) # (B, 1, N, 2) heading_i = heading.unsqueeze(2) # (B, N, 1, 2) heading_j = heading.unsqueeze(1).expand(-1, n_agents, -1, -1) # (B, 1, N, 2) # Compute relative vectors and distance rel_vec = pos_j - pos_i # (B, N, N, 2) dx, dy = rel_vec[..., 0], rel_vec[..., 1] rel_dist = ( torch.linalg.vector_norm(rel_vec, dim=-1, keepdim=True) / 12 ) # (B, N, N, 1) # Relative angle in agent i's frame angle = torch.atan2(dy, dx) - torch.atan2(heading_i[..., 1], heading_i[..., 0]) angle = (angle + np.pi) % (2 * np.pi) - np.pi rel_angle_sin = torch.sin(angle) rel_angle_cos = torch.cos(angle) # Other agent's heading heading_j_cos = heading_j[..., 0] # (B, 1, N) heading_j_sin = heading_j[..., 1] # (B, 1, N) # Edge features for hard attention edge_features = torch.cat( [ rel_dist, # (B, N, N, 1) rel_angle_cos.unsqueeze(-1), # (B, N, N, 1) rel_angle_sin.unsqueeze(-1), # (B, N, N, 1) heading_j_cos.unsqueeze(-1), # (B, N, N, 1) heading_j_sin.unsqueeze(-1), # (B, N, N, 1) action.unsqueeze(1).expand(-1, n_agents, -1, -1), # (B, N, N, 2) ], dim=-1, ) # Broadcast agent embedding for all pairs (except self-pairs) h_i_expanded = h_i.expand(-1, -1, n_agents, -1) # Remove self-pairs using mask mask = ~torch.eye(n_agents, dtype=torch.bool, device=embedding.device) h_i_flat = h_i_expanded[:, mask].reshape( batch_size * n_agents, n_agents - 1, self.embedding_dim ) edge_flat = edge_features[:, mask].reshape( batch_size * n_agents, n_agents - 1, -1 ) # Concatenate agent embedding and edge features hard_input = torch.cat([h_i_flat, edge_flat], dim=-1) # Hard attention forward h_hard = self.hard_mlp(hard_input) hard_logits = self.hard_encoding(h_hard) hard_weights = F.gumbel_softmax(hard_logits, hard=False, tau=0.5, dim=-1)[ ..., 1 ].unsqueeze(2) hard_weights = hard_weights.view(batch_size, n_agents, n_agents - 1) # hard_weights = hard_weights.permute(1, 0, 2) unnorm_rel_dist = torch.linalg.vector_norm(rel_vec, dim=-1, keepdim=True) unnorm_rel_dist = unnorm_rel_dist[:, mask].reshape( batch_size * n_agents, n_agents - 1, 1 ) # ---- Soft attention computation ---- attention_outputs = [] entropy_list = [] combined_w = [] # Goal-relative polar features for soft attention goal_rel_dist = torch.linalg.vector_norm(goal_rel_vec, dim=-1, keepdim=True) goal_angle_global = torch.atan2(goal_rel_vec[..., 1], goal_rel_vec[..., 0]) heading_angle = torch.atan2(heading_i[..., 1], heading_i[..., 0]) goal_rel_angle = goal_angle_global - heading_angle goal_rel_angle = (goal_rel_angle + np.pi) % (2 * np.pi) - np.pi goal_rel_angle_cos = torch.cos(goal_rel_angle).unsqueeze(-1) goal_rel_angle_sin = torch.sin(goal_rel_angle).unsqueeze(-1) goal_polar = torch.cat( [goal_rel_dist, goal_rel_angle_cos, goal_rel_angle_sin], dim=-1 ) # Soft attention edge features (include goal polar) soft_edge_features = torch.cat([edge_features, goal_polar], dim=-1) q = self.q(agent_embed) epsilon = 1e-6 # q = self.q(h_out).reshape(-1, self.args.n_agents, self.args.attention_dim) # (batch_size, n_agents, args.attention_dim) k = self.k(soft_edge_features) v = F.relu(self.v(soft_edge_features)) x = [] for i in range(n_agents): q_i = q[:, i].view(-1, 1, self.embedding_dim) k_b = k[:, i, :, :] k_i = [ k_b[:, j] for j in range(n_agents) if j != i ] # keys of all other agents w.r.t agent i v_b = v[:, i, :, :] v_i = [ v_b[:, j] for j in range(n_agents) if j != i ] # values of all other agents w.r.t agent i k_i = torch.stack( k_i, dim=0 ) # (n_agents - 1, batch_size, args.attention_dim) k_i = k_i.permute(1, 2, 0) # -> (batch_size, embedding_dim, n_agents - 1) v_i = torch.stack(v_i, dim=0) v_i = v_i.permute(1, 2, 0) # (batch_size, 1, attention_dim) * (batch_size, attention_dim，n_agents - 1) = (batch_size, 1，n_agents - 1) score = torch.matmul(q_i, k_i) # scale then softmax for soft weights scaled_score = score / np.sqrt(self.embedding_dim) soft_weight = F.softmax( scaled_score, dim=-1 ) # (batch_size，1, n_agents - 1) # combine soft and hard weights, then weighted sum of values x_i = (v_i * soft_weight * hard_weights[:, i, :].unsqueeze(1)).sum(dim=-1) x.append(x_i) combined_weights = soft_weight * hard_weights[:, i, :].unsqueeze( 1 ) # (B, 1, N-1) combined_w.append(combined_weights) # Normalize for entropy calculation combined_weights_norm = combined_weights / ( combined_weights.sum(dim=-1, keepdim=True) + epsilon ) # Entropy for analysis/logging entropy = ( -(combined_weights_norm * (combined_weights_norm + epsilon).log()) .sum(dim=-1) .mean() ) entropy_list.append(entropy) # concatenate self and attended features per agent x = torch.stack(x, dim=1).reshape( -1, self.embedding_dim ) # (batch_size * n_agents, args.attention_dim) self_embed = agent_embed.reshape(-1, self.embedding_dim) final_input = torch.cat([self_embed, x], dim=-1) output = self.decoding(final_input) mean_entropy = torch.stack(entropy_list).mean() comb_w = torch.stack(combined_w, dim=1).reshape(n_agents, -1) return ( output, hard_logits[..., 1], unnorm_rel_dist, mean_entropy, hard_weights, comb_w, ) ================================================ FILE: robot_nav/models/MARL/Attention/iga.py ================================================ import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from torch_geometric.nn import MessagePassing from torch_geometric.utils import softmax def entropy_from_attention(attn_weights, target_nodes, num_nodes, eps=1e-10): """ Compute mean per-node entropy of incoming softmax attention distributions. Args: attn_weights (Tensor): Per-edge attention probabilities of shape (num_edges,). target_nodes (Tensor): Destination node indices for each edge of shape (num_edges,). num_nodes (int): Total number of nodes/agents. eps (float, optional): Small constant for numerical stability in log. Defaults to 1e-10. Returns: Tensor: Scalar tensor containing the mean entropy across nodes that have at least one incoming edge. Returns 0.0 if no nodes have incoming edges. """ # Compute log(p), safe for zero attn_log = (attn_weights + eps).log() # For each edge, p*log(p) contrib = -(attn_weights * attn_log) # [num_edges] # Sum contributions per node (i.e., sum over incoming edges) entropies = torch.zeros(num_nodes, device=attn_weights.device).index_add_( 0, target_nodes, contrib ) counts = torch.zeros(num_nodes, device=attn_weights.device).index_add_( 0, target_nodes, torch.ones_like(attn_weights) ) # (Optional) Only average over nodes with at least one incoming edge mask = counts > 0 entropies = entropies[mask] / counts[mask] return ( entropies.mean() if mask.any() else torch.tensor(0.0, device=attn_weights.device) ) class GoalAttentionLayer(MessagePassing): """ Message-passing layer with learned attention over goal/edge features. This layer computes attention scores from concatenated node queries and transformed edge attributes, then aggregates value projections with 'add' aggregation. Args: node_dim (int): Dimensionality of node features. edge_dim (int): Dimensionality of edge attributes. out_dim (int): Output dimensionality for projected messages. """ def __init__(self, node_dim, edge_dim, out_dim): super().__init__(aggr="add") # Could use 'mean' or 'max' as well. self.q = torch.nn.Linear(node_dim, out_dim, bias=False) self.k = torch.nn.Linear(edge_dim, out_dim, bias=False) self.v = torch.nn.Linear(edge_dim, out_dim) self.attn_score_layer = nn.Sequential( nn.Linear(node_dim * 2, node_dim), nn.ReLU(), nn.Linear(node_dim, 1), ) self._last_attn_weights = None def forward(self, x, edge_index, edge_attr): """ Run attention-based message passing. Args: x (Tensor): Node features of shape (num_nodes, node_dim). edge_index (LongTensor): Edge indices of shape (2, num_edges) with format [source, target]. edge_attr (Tensor): Edge attributes of shape (num_edges, edge_dim). Returns: tuple: out (Tensor): Updated node features of shape (num_nodes, out_dim). attn_weights (Tensor or None): Last per-edge softmax weights of shape (num_edges,). None if no edges. """ q = self.q(x) out = self.propagate(edge_index, x=q, edge_attr=edge_attr) return out, self._last_attn_weights def message(self, x_i, edge_attr, index, ptr, size_i): """ Compute per-edge messages using attention weights. Args: x_i (Tensor): Target-node queries for each edge, shape (num_edges, out_dim). edge_attr (Tensor): Edge attributes for each edge, shape (num_edges, edge_dim). index (LongTensor): Target node indices per edge, shape (num_edges,). ptr: Unused (PyG internal). size_i: Unused (PyG internal). Returns: Tensor: Per-edge messages of shape (num_edges, out_dim). """ k = F.leaky_relu(self.k(edge_attr)) v = F.leaky_relu(self.v(edge_attr)) attention_input = torch.cat([x_i, k], dim=-1) scores = self.attn_score_layer(attention_input).squeeze(-1) attn_weights = softmax(scores, index) # index = target node index self._last_attn_weights = attn_weights.detach() return v * attn_weights.unsqueeze(-1) def aggregate(self, inputs, index, ptr=None, dim_size=None): """ Aggregate messages per target node. Args: inputs (Tensor): Per-edge messages, shape (num_edges, out_dim). index (LongTensor): Target node indices per edge, shape (num_edges,). ptr: Unused (PyG internal). dim_size (int or None): Number of target nodes (optional). Returns: Tensor: Aggregated node features, shape (num_nodes, out_dim). """ return super().aggregate(inputs, index, ptr, dim_size) class Attention(nn.Module): """ Multi-robot attention mechanism combining hard (binary) and soft (weighted) attentions. The module encodes agent features, derives pairwise geometric edge attributes, computes hard attention masks and soft attention weights, performs message passing, and decodes concatenated self/attended embeddings. Args: embedding_dim (int): Dimension of the agent embedding vector. Attributes: embedding1 (nn.Linear): First layer for agent feature encoding. embedding2 (nn.Linear): Second layer for agent feature encoding. hard_mlp (nn.Sequential): MLP over concatenated node/edge features for hard attention. hard_encoding (nn.Linear): Outputs logits for binary attention (2 classes). q (nn.Linear): Query projection for soft attention. k (nn.Linear): Key projection from edge features for soft attention. v (nn.Linear): Value projection from edge features for soft attention. attn_score_layer (nn.Sequential): Computes unnormalized scores for soft attention. decode_1 (nn.Linear): First decoder layer for concatenated embeddings. decode_2 (nn.Linear): Second decoder layer for concatenated embeddings. message_graph (GoalAttentionLayer): Graph attention/message-passing layer. node_update (nn.Sequential): Optional node-update MLP (unused in forward aggregation). """ def __init__(self, embedding_dim): """ Initialize the attention module. Args: embedding_dim (int): Output embedding dimension per agent. """ super(Attention, self).__init__() self.embedding_dim = embedding_dim self.node_update = nn.Sequential( nn.Linear(embedding_dim * 2, embedding_dim), nn.LeakyReLU(), nn.Linear(embedding_dim, embedding_dim), nn.LeakyReLU(), ) self.message_graph = GoalAttentionLayer( node_dim=embedding_dim, edge_dim=10, out_dim=embedding_dim ) self.embedding1 = nn.Linear(5, 128) nn.init.kaiming_uniform_(self.embedding1.weight, nonlinearity="leaky_relu") self.embedding2 = nn.Linear(128, embedding_dim) nn.init.kaiming_uniform_(self.embedding2.weight, nonlinearity="leaky_relu") # Hard attention MLP with distance self.hard_mlp = nn.Sequential( nn.Linear(embedding_dim + 7, embedding_dim), nn.ReLU(), nn.Linear(embedding_dim, embedding_dim), ) self.hard_encoding = nn.Linear(embedding_dim, 2) # Soft attention projections self.q = nn.Linear(embedding_dim, embedding_dim, bias=False) self.k = nn.Linear(10, embedding_dim, bias=False) self.v = nn.Linear(10, embedding_dim) # Soft attention score network (with polar other robot goal position) self.attn_score_layer = nn.Sequential( nn.Linear(embedding_dim * 2, embedding_dim), nn.ReLU(), nn.Linear(embedding_dim, 1), ) # Decoder self.decode_1 = nn.Linear(embedding_dim * 2, embedding_dim * 2) nn.init.kaiming_uniform_(self.decode_1.weight, nonlinearity="leaky_relu") self.decode_2 = nn.Linear(embedding_dim * 2, embedding_dim * 2) nn.init.kaiming_uniform_(self.decode_2.weight, nonlinearity="leaky_relu") def encode_agent_features(self, embed): """ Encode per-agent features with a two-layer MLP. Args: embed (Tensor): Raw agent features of shape (B*N, 5). Returns: Tensor: Encoded embeddings of shape (B*N, embedding_dim). """ embed = F.leaky_relu(self.embedding1(embed)) embed = F.leaky_relu(self.embedding2(embed)) return embed def forward(self, embedding): """ Compute hard and soft attentions and produce attended embeddings. Args: embedding (Tensor): Input tensor of shape (B, N, D), where D ≥ 11. The first channels are expected to include position (x,y), heading (cos,sin), agent features for encoding, action, and goal. Returns: tuple: att_embedding (Tensor): Attended embedding, shape (B*N, 2*embedding_dim). hard_logits (Tensor): Logits for hard attention (keep class index 1), shape (B*N, N-1). unnorm_rel_dist (Tensor): Unnormalized pairwise distances, shape (B*N, N-1, 1). mean_entropy (Tensor): Scalar mean entropy of soft attention per batch. hard_weights (Tensor): Binary hard attention mask, shape (B, N, N). comb_w (Tensor): Combined soft weights per (receiver, sender), shape (N, N*(N-1)). """ if embedding.dim() == 2: embedding = embedding.unsqueeze(0) batch_size, n_agents, _ = embedding.shape # Extract sub-features embed = embedding[:, :, 4:9].reshape(batch_size * n_agents, -1) position = embedding[:, :, :2].reshape(batch_size, n_agents, 2) heading = embedding[:, :, 2:4].reshape( batch_size, n_agents, 2 ) # assume (cos(θ), sin(θ)) action = embedding[:, :, 7:9].reshape(batch_size, n_agents, 2) goal = embedding[:, :, -2:].reshape(batch_size, n_agents, 2) # Compute pairwise relative goal vectors (for each i,j) goal_j = goal.unsqueeze(1).expand(-1, n_agents, -1, -1) pos_i = position.unsqueeze(2) goal_rel_vec = goal_j - pos_i # Encode agent features agent_embed = self.encode_agent_features(embed) agent_embed = agent_embed.view(batch_size, n_agents, self.embedding_dim) # Prep for hard attention: compute all relative geometry for each agent pair h_i = agent_embed.unsqueeze(2) # (B, N, 1, D) pos_i = position.unsqueeze(2) # (B, N, 1, 2) pos_j = position.unsqueeze(1) # (B, 1, N, 2) heading_i = heading.unsqueeze(2) # (B, N, 1, 2) heading_j = heading.unsqueeze(1).expand(-1, n_agents, -1, -1) # (B, 1, N, 2) # Compute relative vectors and distance rel_vec = pos_j - pos_i # (B, N, N, 2) dx, dy = rel_vec[..., 0], rel_vec[..., 1] rel_dist = ( torch.linalg.vector_norm(rel_vec, dim=-1, keepdim=True) / 12 ) # (B, N, N, 1) # Relative angle in agent i's frame angle = torch.atan2(dy, dx) - torch.atan2(heading_i[..., 1], heading_i[..., 0]) angle = (angle + np.pi) % (2 * np.pi) - np.pi rel_angle_sin = torch.sin(angle) rel_angle_cos = torch.cos(angle) # Other agent's heading heading_j_cos = heading_j[..., 0] # (B, 1, N) heading_j_sin = heading_j[..., 1] # (B, 1, N) # Edge features for hard attention edge_features = torch.cat( [ rel_dist, # (B, N, N, 1) rel_angle_cos.unsqueeze(-1), # (B, N, N, 1) rel_angle_sin.unsqueeze(-1), # (B, N, N, 1) heading_j_cos.unsqueeze(-1), # (B, N, N, 1) heading_j_sin.unsqueeze(-1), # (B, N, N, 1) action.unsqueeze(1).expand(-1, n_agents, -1, -1), # (B, N, N, 2) ], dim=-1, ) # Broadcast agent embedding for all pairs (except self-pairs) h_i_expanded = h_i.expand(-1, -1, n_agents, -1) h_i_flat = h_i_expanded.reshape( batch_size * n_agents, n_agents, self.embedding_dim ) edge_flat = edge_features.reshape(batch_size * n_agents, n_agents, -1) # Concatenate agent embedding and edge features hard_input = torch.cat([h_i_flat, edge_flat], dim=-1) # Hard attention forward h_hard = self.hard_mlp(hard_input) hard_logits = self.hard_encoding(h_hard) hard_weights = F.gumbel_softmax(hard_logits, hard=False, tau=0.2, dim=-1)[ ..., 1 ].unsqueeze(2) hard_weights = hard_weights.view(batch_size, n_agents, n_agents) unnorm_rel_dist = torch.linalg.vector_norm(rel_vec, dim=-1, keepdim=True) unnorm_rel_dist = unnorm_rel_dist.reshape(batch_size * n_agents, n_agents, 1) # ---- Soft attention computation ---- q = self.q(agent_embed).reshape(batch_size * n_agents, -1) attention_outputs = [] entropy_list = [] combined_w = [] # Goal-relative polar features for soft attention goal_rel_dist = torch.linalg.vector_norm(goal_rel_vec, dim=-1, keepdim=True) goal_angle_global = torch.atan2(goal_rel_vec[..., 1], goal_rel_vec[..., 0]) heading_angle = torch.atan2(heading_i[..., 1], heading_i[..., 0]) goal_rel_angle = goal_angle_global - heading_angle goal_rel_angle = (goal_rel_angle + np.pi) % (2 * np.pi) - np.pi goal_rel_angle_cos = torch.cos(goal_rel_angle).unsqueeze(-1) goal_rel_angle_sin = torch.sin(goal_rel_angle).unsqueeze(-1) goal_polar = torch.cat( [goal_rel_dist, goal_rel_angle_cos, goal_rel_angle_sin], dim=-1 ) # Soft attention edge features (include goal polar) soft_edge_features = torch.cat([edge_features, goal_polar], dim=-1) attn_outputs = [] for b in range(batch_size): edge_index_list = [] edge_attr_list = [] # Agent embeddings for this scenario node_feats = agent_embed[b] # [n, emb_dim] soft_feats = soft_edge_features[b] # [n, n, edge_dim] hard_mask = hard_weights[b] # [n, n] # Only build edges where hard attention mask is active, no self-loops for i in range(n_agents): for j in range(n_agents): if i != j and hard_mask[i, j] > 0.5: edge_index_list.append([j, i]) # from j (src) to i (tgt) edge_attr_list.append(soft_feats[i, j]) # Edge tensors if edge_index_list: edge_index = torch.tensor( edge_index_list, dtype=torch.long, device=embedding.device ).t() # [2, num_edges] edge_attr = torch.stack(edge_attr_list, dim=0) # [num_edges, edge_dim] else: edge_index = torch.zeros( (2, 0), dtype=torch.long, device=embedding.device ) edge_attr = torch.zeros( (0, soft_feats.shape[-1]), dtype=soft_feats.dtype, device=embedding.device, ) # Message passing: edge-to-node aggregation only attn_out, attn_weights = self.message_graph( node_feats, edge_index, edge_attr ) # [n, embedding_dim] attn_outputs.append(attn_out) batch_entropy = entropy_from_attention( attn_weights, edge_index[1], num_nodes=n_agents ) entropy_list.append(batch_entropy) combined_weights = torch.zeros( (n_agents, n_agents), device=attn_weights.device ) for idx in range(edge_index.shape[1]): j = edge_index[0, idx].item() # sender/source i = edge_index[1, idx].item() # receiver/target combined_weights[i, j] = attn_weights[idx] combined_w.append(combined_weights) # Re-stack results for full batch (shape: [batch_size, n_agents, embedding_dim]) attn_stack = torch.stack(attn_outputs, dim=0).reshape(batch_size * n_agents, -1) self_embed = agent_embed.reshape(batch_size * n_agents, -1) # Concat original + attended for each agent concat_embed = torch.cat( [self_embed, attn_stack], dim=-1 ) # [batch, n, 2*embedding_dim] x = F.leaky_relu(self.decode_1(concat_embed)) att_embedding = F.leaky_relu(self.decode_2(x)) mean_entropy = torch.stack(entropy_list).mean() comb_w = torch.stack(combined_w, dim=1).reshape(n_agents, -1) return ( att_embedding, hard_logits[..., 1], unnorm_rel_dist, mean_entropy, hard_weights, comb_w, ) ================================================ FILE: robot_nav/models/MARL/__init__.py ================================================ ================================================ FILE: robot_nav/models/MARL/marlTD3/__init__.py ================================================ ================================================ FILE: robot_nav/models/MARL/marlTD3/marlTD3.py ================================================ from pathlib import Path import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from numpy import inf from torch.utils.tensorboard import SummaryWriter from robot_nav.models.MARL.Attention.g2anet import G2ANet from robot_nav.models.MARL.Attention.iga import Attention class Actor(nn.Module): """ Policy network for multi-agent control with an attention encoder. The actor encodes inter-agent context (via IGA or G2ANet attention) and maps the attended embedding to continuous actions. Args: action_dim (int): Number of action dimensions per agent. embedding_dim (int): Dimensionality of the attention embedding. attention (str): Attention backend, one of {"igs", "g2anet"}. Attributes: attention (nn.Module): Attention encoder producing attended embeddings and diagnostics (hard logits, distances, entropy, masks, combined weights). policy_head (nn.Sequential): MLP mapping attended embeddings to actions in [-1, 1]. """ def __init__(self, action_dim, embedding_dim, attention): super().__init__() if attention == "igs": self.attention = Attention(embedding_dim) elif attention == "g2anet": self.attention = G2ANet(embedding_dim) # ➊ edge classifier else: raise ValueError("unknown attention mechanism in Actor") # ➋ policy head (everything _after_ attention) self.policy_head = nn.Sequential( nn.Linear(embedding_dim * 2, 400), nn.LeakyReLU(), nn.Linear(400, 300), nn.LeakyReLU(), nn.Linear(300, action_dim), nn.Tanh(), ) def forward(self, obs, detach_attn=False): """ Run the actor to produce actions and attention diagnostics. Args: obs (Tensor): Observations of shape (B, N, obs_dim) or (N, obs_dim). detach_attn (bool, optional): If True, detaches attention features before the policy head (useful for target policy smoothing). Defaults to False. Returns: tuple: action (Tensor): Predicted actions, shape (B*N, action_dim). hard_logits (Tensor): Hard attention logits, shape (B*N, N-1). pair_d (Tensor): Unnormalized pairwise distances, shape (B*N, N-1, 1). mean_entropy (Tensor): Mean soft-attention entropy (scalar tensor). hard_weights (Tensor): Binary hard attention mask, shape (B, N, N). combined_weights (Tensor): Soft weights per (receiver, sender), shape (N, N*(N-1)) for each batch item (batched internally). """ attn_out, hard_logits, pair_d, mean_entropy, hard_weights, combined_weights = ( self.attention(obs) ) if detach_attn: # used in the policy phase attn_out = attn_out.detach() action = self.policy_head(attn_out) return action, hard_logits, pair_d, mean_entropy, hard_weights, combined_weights class Critic(nn.Module): """ Twin Q-value critic with attention-based state encoding. Computes two independent Q estimates (Q1, Q2) from attended embeddings and concatenated actions, following the TD3 design. Args: action_dim (int): Number of action dimensions per agent. embedding_dim (int): Dimensionality of the attention embedding. attention (str): Attention backend, one of {"igs", "g2anet"}. Attributes: attention (nn.Module): Attention encoder producing attended embeddings and diagnostics (hard logits, distances, entropy, masks). layer_1..layer_6 (nn.Linear): MLP layers forming the twin Q networks. """ def __init__(self, action_dim, embedding_dim, attention): super(Critic, self).__init__() self.embedding_dim = embedding_dim if attention == "igs": self.attention = Attention(embedding_dim) elif attention == "g2anet": self.attention = G2ANet(embedding_dim) # ➊ edge classifier else: raise ValueError("unknown attention mechanism in Critic") self.layer_1 = nn.Linear(self.embedding_dim * 2, 400) torch.nn.init.kaiming_uniform_(self.layer_1.weight, nonlinearity="leaky_relu") self.layer_2_s = nn.Linear(400, 300) torch.nn.init.kaiming_uniform_(self.layer_2_s.weight, nonlinearity="leaky_relu") self.layer_2_a = nn.Linear(action_dim, 300) torch.nn.init.kaiming_uniform_(self.layer_2_a.weight, nonlinearity="leaky_relu") self.layer_3 = nn.Linear(300, 1) torch.nn.init.kaiming_uniform_(self.layer_3.weight, nonlinearity="leaky_relu") self.layer_4 = nn.Linear(self.embedding_dim * 2, 400) torch.nn.init.kaiming_uniform_(self.layer_4.weight, nonlinearity="leaky_relu") self.layer_5_s = nn.Linear(400, 300) torch.nn.init.kaiming_uniform_(self.layer_5_s.weight, nonlinearity="leaky_relu") self.layer_5_a = nn.Linear(action_dim, 300) torch.nn.init.kaiming_uniform_(self.layer_5_a.weight, nonlinearity="leaky_relu") self.layer_6 = nn.Linear(300, 1) torch.nn.init.kaiming_uniform_(self.layer_6.weight, nonlinearity="leaky_relu") def forward(self, embedding, action): """ Compute twin Q values from attended embeddings and actions. Args: embedding (Tensor): Agent embeddings of shape (B, N, state_dim). action (Tensor): Actions of shape (B*N, action_dim). Returns: tuple: Q1 (Tensor): First Q-value estimate, shape (B*N, 1). Q2 (Tensor): Second Q-value estimate, shape (B*N, 1). mean_entropy (Tensor): Mean soft-attention entropy (scalar tensor). hard_logits (Tensor): Hard attention logits, shape (B*N, N-1). unnorm_rel_dist (Tensor): Unnormalized pairwise distances, shape (B*N, N-1, 1). hard_weights (Tensor): Binary hard attention mask, shape (B, N, N). """ ( embedding_with_attention, hard_logits, unnorm_rel_dist, mean_entropy, hard_weights, _, ) = self.attention(embedding) # Q1 s1 = F.leaky_relu(self.layer_1(embedding_with_attention)) s1 = F.leaky_relu(self.layer_2_s(s1) + self.layer_2_a(action)) # ✅ No .data q1 = self.layer_3(s1) # Q2 s2 = F.leaky_relu(self.layer_4(embedding_with_attention)) s2 = F.leaky_relu(self.layer_5_s(s2) + self.layer_5_a(action)) # ✅ No .data q2 = self.layer_6(s2) return q1, q2, mean_entropy, hard_logits, unnorm_rel_dist, hard_weights class TD3(object): """ Twin Delayed DDPG (TD3) agent for multi-agent continuous control. Wraps actor/critic networks, exploration policy, training loop, logging, and checkpointing utilities. Args: state_dim (int): Per-agent state dimension. action_dim (int): Per-agent action dimension. max_action (float): Action clip magnitude (actions are clipped to [-max_action, max_action]). device (torch.device): Target device for models and tensors. num_robots (int): Number of agents. lr_actor (float, optional): Actor learning rate. Defaults to 1e-4. lr_critic (float, optional): Critic learning rate. Defaults to 3e-4. save_every (int, optional): Save frequency in training iterations (0 = disabled). Defaults to 0. load_model (bool, optional): If True, loads weights on init. Defaults to False. save_directory (Path, optional): Directory for saving checkpoints. model_name (str, optional): Base filename for checkpoints. Defaults to "marlTD3". load_model_name (str or None, optional): Filename base to load. Defaults to None (uses model_name). load_directory (Path, optional): Directory to load checkpoints from. attention (str, optional): Attention backend, one of {"igs", "g2anet"}. Defaults to "igs". Attributes: actor (Actor): Policy network. actor_target (Actor): Target policy network. critic (Critic): Twin Q-value network. critic_target (Critic): Target critic network. actor_optimizer (torch.optim.Optimizer): Optimizer over attention + policy head. critic_optimizer (torch.optim.Optimizer): Optimizer over critic. writer (SummaryWriter): TensorBoard writer. iter_count (int): Training iteration counter for logging/saving. save_every (int): Save frequency. model_name (str): Base filename for checkpoints. save_directory (Path): Directory for saving checkpoints. """ def __init__( self, state_dim, action_dim, max_action, device, num_robots, lr_actor=1e-4, lr_critic=3e-4, save_every=0, load_model=False, save_directory=Path("robot_nav/models/MARL/marlTD3/checkpoint"), model_name="marlTD3", load_model_name=None, load_directory=Path("robot_nav/models/MARL/marlTD3/checkpoint"), attention="igs", ): # Initialize the Actor network if attention not in ["igs", "g2anet"]: raise ValueError("unknown attention mechanism specified for TD3 model") self.num_robots = num_robots self.device = device self.actor = Actor(action_dim, embedding_dim=256, attention=attention).to( self.device ) # Using the updated Actor self.actor_target = Actor( action_dim, embedding_dim=256, attention=attention ).to(self.device) self.actor_target.load_state_dict(self.actor.state_dict()) self.attn_params = list(self.actor.attention.parameters()) self.policy_params = list(self.actor.policy_head.parameters()) self.actor_optimizer = torch.optim.Adam( self.policy_params + self.attn_params, lr=lr_actor ) # TD3 policy self.critic = Critic(action_dim, embedding_dim=256, attention=attention).to( self.device ) # Using the updated Critic self.critic_target = Critic( action_dim, embedding_dim=256, attention=attention ).to(self.device) self.critic_target.load_state_dict(self.critic.state_dict()) self.critic_optimizer = torch.optim.Adam( params=self.critic.parameters(), lr=lr_critic ) self.action_dim = action_dim self.max_action = max_action self.state_dim = state_dim self.writer = SummaryWriter(comment=model_name) self.iter_count = 0 if load_model_name is None: load_model_name = model_name if load_model: self.load(filename=load_model_name, directory=load_directory) self.save_every = save_every self.model_name = model_name self.save_directory = save_directory def get_action(self, obs, add_noise): """ Compute an action for the given observation, with optional exploration noise. Args: obs (np.ndarray): Observation array of shape (N, state_dim) or (B, N, state_dim). add_noise (bool): If True, adds Gaussian exploration noise and clips to bounds. Returns: tuple: action (np.ndarray): Actions reshaped to (N, action_dim). connection_logits (Tensor): Hard attention logits from the actor. combined_weights (Tensor): Soft attention weights per (receiver, sender). """ action, connection, combined_weights = self.act(obs) if add_noise: noise = np.random.normal(0, 0.5, size=action.shape) noise = [n / 4 if i % 2 else n for i, n in enumerate(noise)] action = (action + noise).clip(-self.max_action, self.max_action) return action.reshape(-1, 2), connection, combined_weights def act(self, state): """ Compute the deterministic action from the current policy. Args: state (np.ndarray): Observation array of shape (N, state_dim). Returns: tuple: action (np.ndarray): Flattened action vector of shape (N*action_dim,). connection_logits (Tensor): Hard attention logits from the actor. combined_weights (Tensor): Soft attention weights per (receiver, sender). """ # Function to get the action from the actor state = torch.Tensor(state).to(self.device) # res = self.attention(state) action, connection, _, _, _, combined_weights = self.actor(state) return action.cpu().data.numpy().flatten(), connection, combined_weights # training cycle def train( self, replay_buffer, iterations, batch_size, discount=0.99, tau=0.005, policy_noise=0.2, noise_clip=0.5, policy_freq=2, bce_weight=0.01, entropy_weight=1, connection_proximity_threshold=4, max_grad_norm=7.0, ): """ Run a TD3 training loop over sampled mini-batches. Args: replay_buffer: Buffer supporting ``sample_batch(batch_size)`` -> tuple of arrays. iterations (int): Number of gradient steps. batch_size (int): Mini-batch size. discount (float, optional): Discount factor γ. Defaults to 0.99. tau (float, optional): Target network update rate. Defaults to 0.005. policy_noise (float, optional): Std of target policy noise. Defaults to 0.2. noise_clip (float, optional): Clipping range for target noise. Defaults to 0.5. policy_freq (int, optional): Actor update period (in critic steps). Defaults to 2. bce_weight (float, optional): Weight for hard-connection BCE loss. Defaults to 0.01. entropy_weight (float, optional): Weight for attention entropy bonus. Defaults to 1. connection_proximity_threshold (float, optional): Distance threshold for the positive class when supervising hard connections. Defaults to 4. max_grad_norm (float, optional): Gradient clipping norm. Defaults to 7.0. Returns: None """ av_Q = 0 max_Q = -inf av_loss = 0 av_critic_loss = 0 av_critic_entropy = [] av_actor_entropy = [] av_actor_loss = 0 av_critic_bce_loss = [] av_actor_bce_loss = [] for it in range(iterations): # sample a batch ( batch_states, batch_actions, batch_rewards, batch_dones, batch_next_states, ) = replay_buffer.sample_batch(batch_size) state = ( torch.Tensor(batch_states) .to(self.device) .view(batch_size, self.num_robots, self.state_dim) ) next_state = ( torch.Tensor(batch_next_states) .to(self.device) .view(batch_size, self.num_robots, self.state_dim) ) action = ( torch.Tensor(batch_actions) .to(self.device) .view(batch_size * self.num_robots, self.action_dim) ) reward = ( torch.Tensor(batch_rewards) .to(self.device) .view(batch_size * self.num_robots, 1) ) done = ( torch.Tensor(batch_dones) .to(self.device) .view(batch_size * self.num_robots, 1) ) with torch.no_grad(): next_action, _, _, _, _, _ = self.actor_target( next_state, detach_attn=True ) # --- Target smoothing --- noise = ( torch.Tensor(batch_actions) .data.normal_(0, policy_noise) .to(self.device) ).reshape(-1, 2) noise = noise.clamp(-noise_clip, noise_clip) next_action = (next_action + noise).clamp(-self.max_action, self.max_action) # --- Target Q values --- target_Q1, target_Q2, _, _, _, _ = self.critic_target( next_state, next_action ) target_Q = torch.min(target_Q1, target_Q2) av_Q += target_Q.mean() max_Q = max(max_Q, target_Q.max().item()) target_Q = reward + ((1 - done) * discount * target_Q).detach() # --- Critic update --- ( current_Q1, current_Q2, mean_entropy, hard_logits, unnorm_rel_dist, hard_weights, ) = self.critic(state, action) critic_loss = F.mse_loss(current_Q1, target_Q) + F.mse_loss( current_Q2, target_Q ) targets = ( unnorm_rel_dist.flatten() < connection_proximity_threshold ).float() flat_logits = hard_logits.flatten() bce_loss = F.binary_cross_entropy_with_logits(flat_logits, targets) av_critic_bce_loss.append(bce_loss) total_loss = ( critic_loss - entropy_weight * mean_entropy + bce_weight * bce_loss ) av_critic_entropy.append(mean_entropy) self.critic_optimizer.zero_grad() total_loss.backward() torch.nn.utils.clip_grad_norm_(self.critic.parameters(), max_grad_norm) self.critic_optimizer.step() av_loss += total_loss.item() av_critic_loss += critic_loss.item() # --- Actor update --- if it % policy_freq == 0: action, hard_logits, unnorm_rel_dist, mean_entropy, hard_weights, _ = ( self.actor(state, detach_attn=False) ) targets = ( unnorm_rel_dist.flatten() < connection_proximity_threshold ).float() flat_logits = hard_logits.flatten() bce_loss = F.binary_cross_entropy_with_logits(flat_logits, targets) av_actor_bce_loss.append(bce_loss) actor_Q, _, _, _, _, _ = self.critic(state, action) actor_loss = -actor_Q.mean() total_loss = ( actor_loss - entropy_weight * mean_entropy + bce_weight * bce_loss ) av_actor_entropy.append(mean_entropy) self.actor_optimizer.zero_grad() total_loss.backward() torch.nn.utils.clip_grad_norm_(self.policy_params, max_grad_norm) self.actor_optimizer.step() av_actor_loss += total_loss.item() # Soft update target networks for param, target_param in zip( self.actor.parameters(), self.actor_target.parameters() ): target_param.data.copy_( tau * param.data + (1 - tau) * target_param.data ) for param, target_param in zip( self.critic.parameters(), self.critic_target.parameters() ): target_param.data.copy_( tau * param.data + (1 - tau) * target_param.data ) self.iter_count += 1 self.writer.add_scalar( "train/loss_total", av_loss / iterations, self.iter_count ) self.writer.add_scalar( "train/critic_loss", av_critic_loss / iterations, self.iter_count ) self.writer.add_scalar( "train/av_critic_entropy", sum(av_critic_entropy) / len(av_critic_entropy), self.iter_count, ) self.writer.add_scalar( "train/av_actor_entropy", sum(av_actor_entropy) / len(av_actor_entropy), self.iter_count, ) self.writer.add_scalar( "train/av_critic_bce_loss", sum(av_critic_bce_loss) / len(av_critic_bce_loss), self.iter_count, ) self.writer.add_scalar( "train/av_actor_bce_loss", sum(av_actor_bce_loss) / len(av_actor_bce_loss), self.iter_count, ) self.writer.add_scalar("train/avg_Q", av_Q / iterations, self.iter_count) self.writer.add_scalar("train/max_Q", max_Q, self.iter_count) self.writer.add_scalar( "train/actor_loss", av_actor_loss / (iterations // policy_freq), self.iter_count, ) if self.save_every > 0 and self.iter_count % self.save_every == 0: self.save(filename=self.model_name, directory=self.save_directory) def save(self, filename, directory): """ Saves the current model parameters to the specified directory. Args: filename (str): Base filename for saved files. directory (Path): Path to save the model files. """ Path(directory).mkdir(parents=True, exist_ok=True) torch.save(self.actor.state_dict(), "%s/%s_actor.pth" % (directory, filename)) torch.save( self.actor_target.state_dict(), "%s/%s_actor_target.pth" % (directory, filename), ) torch.save(self.critic.state_dict(), "%s/%s_critic.pth" % (directory, filename)) torch.save( self.critic_target.state_dict(), "%s/%s_critic_target.pth" % (directory, filename), ) def load(self, filename, directory): """ Loads model parameters from the specified directory. Args: filename (str): Base filename for saved files. directory (Path): Path to load the model files from. """ self.actor.load_state_dict( torch.load("%s/%s_actor.pth" % (directory, filename)) ) self.actor_target.load_state_dict( torch.load("%s/%s_actor_target.pth" % (directory, filename)) ) self.critic.load_state_dict( torch.load("%s/%s_critic.pth" % (directory, filename)) ) self.critic_target.load_state_dict( torch.load("%s/%s_critic_target.pth" % (directory, filename)) ) print(f"Loaded weights from: {directory}") def prepare_state( self, poses, distance, cos, sin, collision, action, goal_positions ): """ Convert raw environment outputs into per-agent state vectors. Args: poses (list): Per-agent poses [[x, y, theta], ...]. distance (list): Per-agent distances to goal. cos (list): Per-agent cos(heading error to goal). sin (list): Per-agent sin(heading error to goal). collision (list): Per-agent collision flags (bool or {0,1}). action (list): Per-agent last actions [[lin_vel, ang_vel], ...]. goal_positions (list): Per-agent goals [[gx, gy], ...]. Returns: tuple: states (list): Per-agent state vectors (length == state_dim). terminal (list): Terminal flags (collision or goal), same length as states. """ states = [] terminal = [] for i in range(self.num_robots): pose = poses[i] # [x, y, theta] goal_pos = goal_positions[i] # [goal_x, goal_y] act = action[i] # [lin_vel, ang_vel] px, py, theta = pose gx, gy = goal_pos # Heading as cos/sin heading_cos = np.cos(theta) heading_sin = np.sin(theta) # Last velocity lin_vel = act[0] * 2 # Assuming original range [0, 0.5] ang_vel = (act[1] + 1) / 2 # Assuming original range [-1, 1] # Final state vector state = [ px, py, heading_cos, heading_sin, distance[i] / 17, cos[i], sin[i], lin_vel, ang_vel, gx, gy, ] assert ( len(state) == self.state_dim ), f"State length mismatch: expected {self.state_dim}, got {len(state)}" states.append(state) terminal.append(collision[i]) return states, terminal ================================================ FILE: robot_nav/models/PPO/PPO.py ================================================ import torch import torch.nn as nn from torch.distributions import MultivariateNormal import numpy as np from torch.utils.tensorboard import SummaryWriter from pathlib import Path from numpy import inf class RolloutBuffer: """ Buffer to store rollout data (transitions) for PPO training. Attributes: actions (list): Actions taken by the agent. states (list): States observed by the agent. logprobs (list): Log probabilities of the actions. rewards (list): Rewards received from the environment. state_values (list): Value estimates for the states. is_terminals (list): Flags indicating episode termination. """ def __init__(self): """ Initialize empty lists to store buffer elements. """ self.actions = [] self.states = [] self.logprobs = [] self.rewards = [] self.state_values = [] self.is_terminals = [] def clear(self): """ Clear all stored data from the buffer. """ del self.actions[:] del self.states[:] del self.logprobs[:] del self.rewards[:] del self.state_values[:] del self.is_terminals[:] def add(self, state, action, reward, terminal, next_state): """ Add a transition to the buffer. (Partial implementation.) Args: state (list or np.array): The current observed state. action (list or np.array): The action taken. reward (float): The reward received after taking the action. terminal (bool): Whether the episode terminated. next_state (list or np.array): The resulting state after taking the action. """ self.states.append(state) self.rewards.append(reward) self.is_terminals.append(terminal) class ActorCritic(nn.Module): """ Actor-Critic neural network model for PPO. Attributes: actor (nn.Sequential): Policy network (actor) to output action mean. critic (nn.Sequential): Value network (critic) to predict state values. action_var (Tensor): Diagonal covariance matrix for action distribution. device (str): Device used for computation ('cpu' or 'cuda'). max_action (float): Clipping range for action values. """ def __init__(self, state_dim, action_dim, action_std_init, max_action, device): """ Initialize the Actor and Critic networks. Args: state_dim (int): Dimension of the input state. action_dim (int): Dimension of the action space. action_std_init (float): Initial standard deviation of the action distribution. max_action (float): Maximum value allowed for an action (clipping range). device (str): Device to run the model on. """ super(ActorCritic, self).__init__() self.device = device self.max_action = max_action self.action_dim = action_dim self.action_var = torch.full( (action_dim,), action_std_init * action_std_init ).to(self.device) # actor self.actor = nn.Sequential( nn.Linear(state_dim, 400), nn.Tanh(), nn.Linear(400, 300), nn.Tanh(), nn.Linear(300, action_dim), nn.Tanh(), ) # critic self.critic = nn.Sequential( nn.Linear(state_dim, 400), nn.Tanh(), nn.Linear(400, 300), nn.Tanh(), nn.Linear(300, 1), ) def set_action_std(self, new_action_std): """ Set a new standard deviation for the action distribution. Args: new_action_std (float): New standard deviation. """ self.action_var = torch.full( (self.action_dim,), new_action_std * new_action_std ).to(self.device) def forward(self): """ Forward method is not implemented, as it's unused directly. Raises: NotImplementedError: Always raised when called. """ raise NotImplementedError def act(self, state, sample): """ Compute an action, its log probability, and the state value. Args: state (Tensor): Input state tensor. sample (bool): Whether to sample from the action distribution or use mean. Returns: (Tuple[Tensor, Tensor, Tensor]): Sampled (or mean) action, log probability, and state value. """ action_mean = self.actor(state) cov_mat = torch.diag(self.action_var).unsqueeze(dim=0) dist = MultivariateNormal(action_mean, cov_mat) if sample: action = torch.clip( dist.sample(), min=-self.max_action, max=self.max_action ) else: action = dist.mean action_logprob = dist.log_prob(action) state_val = self.critic(state) return action.detach(), action_logprob.detach(), state_val.detach() def evaluate(self, state, action): """ Evaluate action log probabilities, entropy, and state values for given states and actions. Args: state (Tensor): Batch of states. action (Tensor): Batch of actions. Returns: (Tuple[Tensor, Tensor, Tensor]): Action log probabilities, state values, and distribution entropy. """ action_mean = self.actor(state) action_var = self.action_var.expand_as(action_mean) cov_mat = torch.diag_embed(action_var).to(self.device) dist = MultivariateNormal(action_mean, cov_mat) # For Single Action Environments. if self.action_dim == 1: action = action.reshape(-1, self.action_dim) action_logprobs = dist.log_prob(action) dist_entropy = dist.entropy() state_values = self.critic(state) return action_logprobs, state_values, dist_entropy class PPO: """ Proximal Policy Optimization (PPO) implementation for continuous control tasks. Attributes: max_action (float): Maximum action value. action_std (float): Standard deviation of the action distribution. action_std_decay_rate (float): Rate at which to decay action standard deviation. min_action_std (float): Minimum allowed action standard deviation. state_dim (int): Dimension of the state space. gamma (float): Discount factor for future rewards. eps_clip (float): Clipping range for policy updates. device (str): Device for model computation ('cpu' or 'cuda'). save_every (int): Interval (in iterations) for saving model checkpoints. model_name (str): Name used when saving/loading model. save_directory (Path): Directory to save model checkpoints. iter_count (int): Number of training iterations completed. buffer (RolloutBuffer): Buffer to store trajectories. policy (ActorCritic): Current actor-critic network. optimizer (torch.optim.Optimizer): Optimizer for actor and critic. policy_old (ActorCritic): Old actor-critic network for computing PPO updates. MseLoss (nn.Module): Mean squared error loss function. writer (SummaryWriter): TensorBoard summary writer. """ def __init__( self, state_dim, action_dim, max_action, lr_actor=0.0003, lr_critic=0.001, gamma=0.99, eps_clip=0.2, action_std_init=0.6, action_std_decay_rate=0.015, min_action_std=0.1, device="cpu", save_every=10, load_model=False, save_directory=Path("robot_nav/models/PPO/checkpoint"), model_name="PPO", load_directory=Path("robot_nav/models/PPO/checkpoint"), ): self.max_action = max_action self.action_std = action_std_init self.action_std_decay_rate = action_std_decay_rate self.min_action_std = min_action_std self.state_dim = state_dim self.gamma = gamma self.eps_clip = eps_clip self.device = device self.save_every = save_every self.model_name = model_name self.save_directory = save_directory self.iter_count = 0 self.buffer = RolloutBuffer() self.policy = ActorCritic( state_dim, action_dim, action_std_init, self.max_action, self.device ).to(device) self.optimizer = torch.optim.Adam( [ {"params": self.policy.actor.parameters(), "lr": lr_actor}, {"params": self.policy.critic.parameters(), "lr": lr_critic}, ] ) self.policy_old = ActorCritic( state_dim, action_dim, action_std_init, self.max_action, self.device ).to(device) self.policy_old.load_state_dict(self.policy.state_dict()) if load_model: self.load(filename=model_name, directory=load_directory) self.MseLoss = nn.MSELoss() self.writer = SummaryWriter(comment=model_name) def set_action_std(self, new_action_std): """ Set a new standard deviation for the action distribution. Args: new_action_std (float): New standard deviation value. """ self.action_std = new_action_std self.policy.set_action_std(new_action_std) self.policy_old.set_action_std(new_action_std) def decay_action_std(self, action_std_decay_rate, min_action_std): """ Decay the action standard deviation by a fixed rate, down to a minimum threshold. Args: action_std_decay_rate (float): Amount to reduce standard deviation by. min_action_std (float): Minimum value for standard deviation. """ print( "--------------------------------------------------------------------------------------------" ) self.action_std = self.action_std - action_std_decay_rate self.action_std = round(self.action_std, 4) if self.action_std <= min_action_std: self.action_std = min_action_std print( "setting actor output action_std to min_action_std : ", self.action_std ) else: print("setting actor output action_std to : ", self.action_std) self.set_action_std(self.action_std) print( "--------------------------------------------------------------------------------------------" ) def get_action(self, state, add_noise): """ Sample an action using the current policy (optionally with noise), and store in buffer if noise is added. Args: state (array_like): Input state for the policy. add_noise (bool): Whether to sample from the distribution (True) or use the deterministic mean (False). Returns: (np.ndarray): Sampled action. """ with torch.no_grad(): state = torch.FloatTensor(state).to(self.device) action, action_logprob, state_val = self.policy_old.act(state, add_noise) if add_noise: # self.buffer.states.append(state) self.buffer.actions.append(action) self.buffer.logprobs.append(action_logprob) self.buffer.state_values.append(state_val) return action.detach().cpu().numpy().flatten() def train(self, replay_buffer, iterations, batch_size): """ Train the policy and value function using PPO loss based on the stored rollout buffer. Args: replay_buffer (object): Placeholder for compatibility (not used). iterations (int): Number of epochs to optimize the policy per update. batch_size (int): Batch size (not used; training uses the whole buffer). """ # Monte Carlo estimate of returns rewards = [] discounted_reward = 0 for reward, is_terminal in zip( reversed(self.buffer.rewards), reversed(self.buffer.is_terminals) ): if is_terminal: discounted_reward = 0 discounted_reward = reward + (self.gamma * discounted_reward) rewards.insert(0, discounted_reward) # Normalizing the rewards rewards = torch.tensor(rewards, dtype=torch.float32).to(self.device) rewards = (rewards - rewards.mean()) / (rewards.std() + 1e-7) # convert list to tensor assert len(self.buffer.actions) == len(self.buffer.states) states = [torch.tensor(st, dtype=torch.float32) for st in self.buffer.states] old_states = torch.squeeze(torch.stack(states, dim=0)).detach().to(self.device) old_actions = ( torch.squeeze(torch.stack(self.buffer.actions, dim=0)) .detach() .to(self.device) ) old_logprobs = ( torch.squeeze(torch.stack(self.buffer.logprobs, dim=0)) .detach() .to(self.device) ) old_state_values = ( torch.squeeze(torch.stack(self.buffer.state_values, dim=0)) .detach() .to(self.device) ) # calculate advantages advantages = rewards.detach() - old_state_values.detach() av_state_values = 0 max_state_value = -inf av_loss = 0 # Optimize policy for K epochs for _ in range(iterations): # Evaluating old actions and values logprobs, state_values, dist_entropy = self.policy.evaluate( old_states, old_actions ) # match state_values tensor dimensions with rewards tensor state_values = torch.squeeze(state_values) av_state_values += torch.mean(state_values) max_state_value = max(max_state_value, max(state_values)) # Finding the ratio (pi_theta / pi_theta__old) ratios = torch.exp(logprobs - old_logprobs.detach()) # Finding Surrogate Loss surr1 = ratios * advantages surr2 = ( torch.clamp(ratios, 1 - self.eps_clip, 1 + self.eps_clip) * advantages ) # final loss of clipped objective PPO loss = ( -torch.min(surr1, surr2) + 0.5 * self.MseLoss(state_values, rewards) - 0.01 * dist_entropy ) # take gradient step self.optimizer.zero_grad() loss.mean().backward() self.optimizer.step() av_loss += loss.mean() # Copy new weights into old policy self.policy_old.load_state_dict(self.policy.state_dict()) # clear buffer self.buffer.clear() self.decay_action_std(self.action_std_decay_rate, self.min_action_std) self.iter_count += 1 # Write new values for tensorboard self.writer.add_scalar("train/loss", av_loss / iterations, self.iter_count) self.writer.add_scalar( "train/avg_value", av_state_values / iterations, self.iter_count ) self.writer.add_scalar("train/max_value", max_state_value, self.iter_count) if self.save_every > 0 and self.iter_count % self.save_every == 0: self.save(filename=self.model_name, directory=self.save_directory) def prepare_state(self, latest_scan, distance, cos, sin, collision, goal, action): """ Convert raw sensor and navigation data into a normalized state vector for the policy. Args: latest_scan (list[float]): LIDAR scan data. distance (float): Distance to the goal. cos (float): Cosine of angle to the goal. sin (float): Sine of angle to the goal. collision (bool): Whether the robot has collided. goal (bool): Whether the robot has reached the goal. action (tuple[float, float]): Last action taken (linear and angular velocities). Returns: (tuple[list[float], int]): Processed state vector and terminal flag (1 if terminal, else 0). """ latest_scan = np.array(latest_scan) inf_mask = np.isinf(latest_scan) latest_scan[inf_mask] = 7.0 max_bins = self.state_dim - 5 bin_size = int(np.ceil(len(latest_scan) / max_bins)) # Initialize the list to store the minimum values of each bin min_values = [] # Loop through the data and create bins for i in range(0, len(latest_scan), bin_size): # Get the current bin bin = latest_scan[i : i + min(bin_size, len(latest_scan) - i)] # Find the minimum value in the current bin and append it to the min_values list min_values.append(min(bin) / 7) # Normalize to [0, 1] range distance /= 10 lin_vel = action[0] * 2 ang_vel = (action[1] + 1) / 2 state = min_values + [distance, cos, sin] + [lin_vel, ang_vel] assert len(state) == self.state_dim terminal = 1 if collision or goal else 0 return state, terminal def save(self, filename, directory): """ Save the current policy model to the specified directory. Args: filename (str): Base name of the model file. directory (Path): Directory to save the model to. """ Path(directory).mkdir(parents=True, exist_ok=True) torch.save( self.policy_old.state_dict(), "%s/%s_policy.pth" % (directory, filename) ) def load(self, filename, directory): """ Load the policy model from a saved checkpoint. Args: filename (str): Base name of the model file. directory (Path): Directory to load the model from. """ self.policy_old.load_state_dict( torch.load( "%s/%s_policy.pth" % (directory, filename), map_location=lambda storage, loc: storage, ) ) self.policy.load_state_dict( torch.load( "%s/%s_policy.pth" % (directory, filename), map_location=lambda storage, loc: storage, ) ) print(f"Loaded weights from: {directory}") ================================================ FILE: robot_nav/models/PPO/__init__.py ================================================ ================================================ FILE: robot_nav/models/RCPG/RCPG.py ================================================ from pathlib import Path import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from numpy import inf from torch.utils.tensorboard import SummaryWriter class Actor(nn.Module): """ Actor network that outputs continuous actions for a given state input. Architecture: - Processes 1D laser scan inputs through 3 convolutional layers. - Embeds goal and previous action inputs using fully connected layers. - Combines all features and passes them through an RNN (GRU, LSTM, or RNN). - Outputs action values via a fully connected feedforward head with Tanh activation. Parameters ---------- action_dim : int Dimensionality of the action space. rnn : str, optional Type of RNN layer to use ("lstm", "gru", or "rnn"). """ def __init__(self, action_dim, rnn="gru"): super(Actor, self).__init__() assert rnn in ["lstm", "gru", "rnn"], "Unsupported rnn type" self.cnn1 = nn.Conv1d(1, 4, kernel_size=8, stride=4) self.cnn2 = nn.Conv1d(4, 8, kernel_size=8, stride=4) self.cnn3 = nn.Conv1d(8, 4, kernel_size=4, stride=2) self.goal_embed = nn.Linear(3, 10) self.action_embed = nn.Linear(2, 10) if rnn == "lstm": self.rnn = nn.LSTM( input_size=36, hidden_size=36, num_layers=1, batch_first=True ) elif rnn == "gru": self.rnn = nn.GRU( input_size=36, hidden_size=36, num_layers=1, batch_first=True ) else: self.rnn = nn.RNN( input_size=36, hidden_size=36, num_layers=1, batch_first=True ) self.layer_1 = nn.Linear(36, 400) torch.nn.init.kaiming_uniform_(self.layer_1.weight, nonlinearity="leaky_relu") self.layer_2 = nn.Linear(400, 300) torch.nn.init.kaiming_uniform_(self.layer_2.weight, nonlinearity="leaky_relu") self.layer_3 = nn.Linear(300, action_dim) self.tanh = nn.Tanh() def forward(self, s): if len(s.shape) == 2: s = s.unsqueeze(0) batch_n, hist_n, state_n = s.shape s = s.reshape(batch_n * hist_n, state_n) laser = s[:, :-5] goal = s[:, -5:-2] act = s[:, -2:] laser = laser.unsqueeze(1) l = F.leaky_relu(self.cnn1(laser)) l = F.leaky_relu(self.cnn2(l)) l = F.leaky_relu(self.cnn3(l)) l = l.flatten(start_dim=1) g = F.leaky_relu(self.goal_embed(goal)) a = F.leaky_relu(self.action_embed(act)) s = torch.concat((l, g, a), dim=-1) s = s.reshape(batch_n, hist_n, -1) output, _ = self.rnn(s) last_output = output[:, -1, :] s = F.leaky_relu(self.layer_1(last_output)) s = F.leaky_relu(self.layer_2(s)) a = self.tanh(self.layer_3(s)) return a class Critic(nn.Module): """ Critic network that estimates Q-values for state-action pairs. Architecture: - Processes the same input as the Actor (laser scan, goal, and previous action). - Uses two separate Q-networks (double Q-learning) for stability. - Each Q-network receives both the RNN-processed state and current action. Parameters ---------- action_dim : int Dimensionality of the action space. rnn : str, optional Type of RNN layer to use ("lstm", "gru", or "rnn"). """ def __init__(self, action_dim, rnn="gru"): super(Critic, self).__init__() assert rnn in ["lstm", "gru", "rnn"], "Unsupported rnn type" self.cnn1 = nn.Conv1d(1, 4, kernel_size=8, stride=4) self.cnn2 = nn.Conv1d(4, 8, kernel_size=8, stride=4) self.cnn3 = nn.Conv1d(8, 4, kernel_size=4, stride=2) self.goal_embed = nn.Linear(3, 10) self.action_embed = nn.Linear(2, 10) if rnn == "lstm": self.rnn = nn.LSTM( input_size=36, hidden_size=36, num_layers=1, batch_first=True ) elif rnn == "gru": self.rnn = nn.GRU( input_size=36, hidden_size=36, num_layers=1, batch_first=True ) else: self.rnn = nn.RNN( input_size=36, hidden_size=36, num_layers=1, batch_first=True ) self.layer_1 = nn.Linear(36, 400) torch.nn.init.kaiming_uniform_(self.layer_1.weight, nonlinearity="leaky_relu") self.layer_2_s = nn.Linear(400, 300) torch.nn.init.kaiming_uniform_(self.layer_2_s.weight, nonlinearity="leaky_relu") self.layer_2_a = nn.Linear(action_dim, 300) torch.nn.init.kaiming_uniform_(self.layer_2_a.weight, nonlinearity="leaky_relu") self.layer_3 = nn.Linear(300, 1) torch.nn.init.kaiming_uniform_(self.layer_3.weight, nonlinearity="leaky_relu") self.layer_4 = nn.Linear(36, 400) torch.nn.init.kaiming_uniform_(self.layer_1.weight, nonlinearity="leaky_relu") self.layer_5_s = nn.Linear(400, 300) torch.nn.init.kaiming_uniform_(self.layer_5_s.weight, nonlinearity="leaky_relu") self.layer_5_a = nn.Linear(action_dim, 300) torch.nn.init.kaiming_uniform_(self.layer_5_a.weight, nonlinearity="leaky_relu") self.layer_6 = nn.Linear(300, 1) torch.nn.init.kaiming_uniform_(self.layer_6.weight, nonlinearity="leaky_relu") def forward(self, s, action): batch_n, hist_n, state_n = s.shape s = s.reshape(batch_n * hist_n, state_n) laser = s[:, :-5] goal = s[:, -5:-2] act = s[:, -2:] laser = laser.unsqueeze(1) l = F.leaky_relu(self.cnn1(laser)) l = F.leaky_relu(self.cnn2(l)) l = F.leaky_relu(self.cnn3(l)) l = l.flatten(start_dim=1) g = F.leaky_relu(self.goal_embed(goal)) a = F.leaky_relu(self.action_embed(act)) s = torch.concat((l, g, a), dim=-1) s = s.reshape(batch_n, hist_n, -1) output, _ = self.rnn(s) last_output = output[:, -1, :] s1 = F.leaky_relu(self.layer_1(last_output)) self.layer_2_s(s1) self.layer_2_a(action) s11 = torch.mm(s1, self.layer_2_s.weight.data.t()) s12 = torch.mm(action, self.layer_2_a.weight.data.t()) s1 = F.leaky_relu(s11 + s12 + self.layer_2_a.bias.data) q1 = self.layer_3(s1) s2 = F.leaky_relu(self.layer_4(last_output)) self.layer_5_s(s2) self.layer_5_a(action) s21 = torch.mm(s2, self.layer_5_s.weight.data.t()) s22 = torch.mm(action, self.layer_5_a.weight.data.t()) s2 = F.leaky_relu(s21 + s22 + self.layer_5_a.bias.data) q2 = self.layer_6(s2) return q1, q2 # RCPG network class RCPG(object): """ Recurrent Convolutional Policy Gradient (RCPG) agent for continuous control tasks. This class implements a recurrent actor-critic architecture using twin Q-networks and soft target updates. It includes model initialization, training, inference, saving/loading, and ROS-based state preparation. Parameters ---------- state_dim : int Dimensionality of the input state. action_dim : int Dimensionality of the action space. max_action : float Maximum allowable action value. device : torch.device Device to run the model on (e.g., 'cuda' or 'cpu'). lr : float, optional Learning rate for actor and critic optimizers. Default is 1e-4. save_every : int, optional Frequency (in iterations) to save model checkpoints. Default is 0 (disabled). load_model : bool, optional Whether to load pretrained model weights. Default is False. save_directory : Path, optional Directory where models are saved. Default is "robot_nav/models/RCPG/checkpoint". model_name : str, optional Name prefix for model checkpoint files. Default is "RCPG". load_directory : Path, optional Directory to load pretrained models from. Default is "robot_nav/models/RCPG/checkpoint". rnn : str, optional Type of RNN to use in networks ("lstm", "gru", or "rnn"). Default is "gru". """ def __init__( self, state_dim, action_dim, max_action, device, lr=1e-4, save_every=0, load_model=False, save_directory=Path("robot_nav/models/RCPG/checkpoint"), model_name="RCPG", load_directory=Path("robot_nav/models/RCPG/checkpoint"), rnn="gru", ): # Initialize the Actor network self.device = device self.actor = Actor(action_dim, rnn).to(self.device) self.actor_target = Actor(action_dim, rnn).to(self.device) self.actor_target.load_state_dict(self.actor.state_dict()) self.actor_optimizer = torch.optim.Adam(params=self.actor.parameters(), lr=lr) # Initialize the Critic networks self.critic = Critic(action_dim, rnn).to(self.device) self.critic_target = Critic(action_dim, rnn).to(self.device) self.critic_target.load_state_dict(self.critic.state_dict()) self.critic_optimizer = torch.optim.Adam(params=self.critic.parameters(), lr=lr) self.action_dim = action_dim self.max_action = max_action self.state_dim = state_dim self.writer = SummaryWriter(comment=model_name) self.iter_count = 0 self.model_name = model_name + rnn if load_model: self.load(filename=self.model_name, directory=load_directory) self.save_every = save_every self.save_directory = save_directory def get_action(self, obs, add_noise): """ Computes an action for the given observation, with optional exploration noise. Parameters ---------- obs : array_like Input observation (state). add_noise : bool If True, adds Gaussian noise for exploration. Returns ------- np.ndarray Action vector clipped to [-max_action, max_action]. """ if add_noise: return ( self.act(obs) + np.random.normal(0, 0.2, size=self.action_dim) ).clip(-self.max_action, self.max_action) else: return self.act(obs) def act(self, state): """ Returns the actor network's raw output for a given input state. Parameters ---------- state : array_like State input. Returns ------- np.ndarray Deterministic action vector from actor network. """ # Function to get the action from the actor state = torch.Tensor(state).to(self.device) return self.actor(state).cpu().data.numpy().flatten() # training cycle def train( self, replay_buffer, iterations, batch_size, discount=0.99, tau=0.005, policy_noise=0.2, noise_clip=0.5, policy_freq=2, ): """ Performs training over a number of iterations using batches from a replay buffer. Parameters ---------- replay_buffer : object Experience replay buffer with a sample_batch method. iterations : int Number of training iterations. batch_size : int Size of each training batch. discount : float, optional Discount factor for future rewards (γ). Default is 0.99. tau : float, optional Soft update parameter for target networks. Default is 0.005. policy_noise : float, optional Standard deviation of noise added to target actions. Default is 0.2. noise_clip : float, optional Range to clip the noise. Default is 0.5. policy_freq : int, optional Frequency of policy updates relative to critic updates. Default is 2. """ av_Q = 0 max_Q = -inf av_loss = 0 for it in range(iterations): # sample a batch from the replay buffer ( batch_states, batch_actions, batch_rewards, batch_dones, batch_next_states, ) = replay_buffer.sample_batch(batch_size) state = torch.Tensor(batch_states).to(self.device) next_state = torch.Tensor(batch_next_states).to(self.device) action = torch.Tensor(batch_actions).to(self.device) reward = torch.Tensor(batch_rewards).to(self.device) done = torch.Tensor(batch_dones).to(self.device) # Obtain the estimated action from the next state by using the actor-target next_action = self.actor_target(next_state) # Add noise to the action noise = ( torch.Tensor(batch_actions) .data.normal_(0, policy_noise) .to(self.device) ) noise = noise.clamp(-noise_clip, noise_clip) next_action = (next_action + noise).clamp(-self.max_action, self.max_action) # Calculate the Q values from the critic-target network for the next state-action pair target_Q1, target_Q2 = self.critic_target(next_state, next_action) # Select the minimal Q value from the 2 calculated values target_Q = torch.min(target_Q1, target_Q2) av_Q += torch.mean(target_Q) max_Q = max(max_Q, torch.max(target_Q)) # Calculate the final Q value from the target network parameters by using Bellman equation target_Q = reward + ((1 - done) * discount * target_Q).detach() # Get the Q values of the basis networks with the current parameters current_Q1, current_Q2 = self.critic(state, action) # Calculate the loss between the current Q value and the target Q value loss = F.mse_loss(current_Q1, target_Q) + F.mse_loss(current_Q2, target_Q) # Perform the gradient descent self.critic_optimizer.zero_grad() loss.backward() self.critic_optimizer.step() if it % policy_freq == 0: # Maximize the actor output value by performing gradient descent on negative Q values # (essentially perform gradient ascent) actor_grad, _ = self.critic(state, self.actor(state)) actor_grad = -actor_grad.mean() self.actor_optimizer.zero_grad() actor_grad.backward() self.actor_optimizer.step() # Use soft update to update the actor-target network parameters by # infusing small amount of current parameters for param, target_param in zip( self.actor.parameters(), self.actor_target.parameters() ): target_param.data.copy_( tau * param.data + (1 - tau) * target_param.data ) # Use soft update to update the critic-target network parameters by infusing # small amount of current parameters for param, target_param in zip( self.critic.parameters(), self.critic_target.parameters() ): target_param.data.copy_( tau * param.data + (1 - tau) * target_param.data ) av_loss += loss self.iter_count += 1 # Write new values for tensorboard self.writer.add_scalar("train/loss", av_loss / iterations, self.iter_count) self.writer.add_scalar("train/avg_Q", av_Q / iterations, self.iter_count) self.writer.add_scalar("train/max_Q", max_Q, self.iter_count) if self.save_every > 0 and self.iter_count % self.save_every == 0: self.save(filename=self.model_name, directory=self.save_directory) def save(self, filename, directory): """ Saves actor and critic model weights to disk. Parameters ---------- filename : str Base name for saved model files. directory : str or Path Target directory to save the models. """ Path(directory).mkdir(parents=True, exist_ok=True) torch.save(self.actor.state_dict(), "%s/%s_actor.pth" % (directory, filename)) torch.save( self.actor_target.state_dict(), "%s/%s_actor_target.pth" % (directory, filename), ) torch.save(self.critic.state_dict(), "%s/%s_critic.pth" % (directory, filename)) torch.save( self.critic_target.state_dict(), "%s/%s_critic_target.pth" % (directory, filename), ) def load(self, filename, directory): """ Loads model weights for actor and critic networks from disk. Parameters ---------- filename : str Base name of saved model files. directory : str or Path Directory from which to load model files. """ self.actor.load_state_dict( torch.load("%s/%s_actor.pth" % (directory, filename)) ) self.actor_target.load_state_dict( torch.load("%s/%s_actor_target.pth" % (directory, filename)) ) self.critic.load_state_dict( torch.load("%s/%s_critic.pth" % (directory, filename)) ) self.critic_target.load_state_dict( torch.load("%s/%s_critic_target.pth" % (directory, filename)) ) print(f"Loaded weights from: {directory}") def prepare_state(self, latest_scan, distance, cos, sin, collision, goal, action): """ Converts raw sensor and environment data into a normalized input state vector. Parameters ---------- latest_scan : list or np.ndarray Laser scan data. distance : float Distance to the goal. cos : float Cosine of the heading angle. sin : float Sine of the heading angle. collision : bool Whether a collision has occurred. goal : bool Whether the goal has been reached. action : list or np.ndarray Previous action taken [linear, angular]. Returns ------- state : list Normalized input state vector. terminal : int Terminal flag: 1 if goal reached or collision, otherwise 0. """ latest_scan = np.array(latest_scan) inf_mask = np.isinf(latest_scan) latest_scan[inf_mask] = 7.0 latest_scan /= 7 # Normalize to [0, 1] range distance /= 10 lin_vel = action[0] * 2 ang_vel = (action[1] + 1) / 2 state = latest_scan.tolist() + [distance, cos, sin] + [lin_vel, ang_vel] assert len(state) == self.state_dim terminal = 1 if collision or goal else 0 return state, terminal ================================================ FILE: robot_nav/models/RCPG/__init__.py ================================================ ================================================ FILE: robot_nav/models/SAC/SAC.py ================================================ from pathlib import Path import numpy as np import torch import torch.nn.functional as F from statistics import mean import robot_nav.models.SAC.SAC_utils as utils from robot_nav.models.SAC.SAC_critic import DoubleQCritic as critic_model from robot_nav.models.SAC.SAC_actor import DiagGaussianActor as actor_model from torch.utils.tensorboard import SummaryWriter class SAC(object): """ Soft Actor-Critic (SAC) implementation. This class implements the SAC algorithm using a Gaussian policy actor and double Q-learning critic. It supports automatic entropy tuning, model saving/loading, and logging via TensorBoard. Args: state_dim (int): Dimension of the observation/state space. action_dim (int): Dimension of the action space. device (torch.device): PyTorch device (e.g., 'cpu' or 'cuda'). max_action (float): Maximum magnitude of actions. discount (float): Discount factor for rewards. init_temperature (float): Initial entropy temperature. alpha_lr (float): Learning rate for entropy temperature alpha. alpha_betas (tuple): Adam optimizer betas for alpha. actor_lr (float): Learning rate for actor network. actor_betas (tuple): Adam optimizer betas for actor. actor_update_frequency (int): Frequency of actor updates. critic_lr (float): Learning rate for critic network. critic_betas (tuple): Adam optimizer betas for critic. critic_tau (float): Soft update parameter for critic target. critic_target_update_frequency (int): Frequency of critic target updates. learnable_temperature (bool): Whether alpha is learnable. save_every (int): Save model every N training steps. Set 0 to disable. load_model (bool): Whether to load model from disk at init. log_dist_and_hist (bool): Log distribution and histogram if True. save_directory (Path): Directory to save models. model_name (str): Name for model checkpoints. load_directory (Path): Directory to load model checkpoints from. """ def __init__( self, state_dim, action_dim, device, max_action, discount=0.99, init_temperature=0.1, alpha_lr=1e-4, alpha_betas=(0.9, 0.999), actor_lr=1e-4, actor_betas=(0.9, 0.999), actor_update_frequency=1, critic_lr=1e-4, critic_betas=(0.9, 0.999), critic_tau=0.005, critic_target_update_frequency=2, learnable_temperature=True, save_every=0, load_model=False, log_dist_and_hist=False, save_directory=Path("robot_nav/models/SAC/checkpoint"), model_name="SAC", load_directory=Path("robot_nav/models/SAC/checkpoint"), ): super().__init__() self.state_dim = state_dim self.action_dim = action_dim self.action_range = (-max_action, max_action) self.device = device self.discount = discount self.critic_tau = critic_tau self.actor_update_frequency = actor_update_frequency self.critic_target_update_frequency = critic_target_update_frequency self.learnable_temperature = learnable_temperature self.save_every = save_every self.model_name = model_name self.save_directory = save_directory self.log_dist_and_hist = log_dist_and_hist self.train_metrics_dict = { "train_critic/loss_av": [], "train_actor/loss_av": [], "train_actor/target_entropy_av": [], "train_actor/entropy_av": [], "train_alpha/loss_av": [], "train_alpha/value_av": [], "train/batch_reward_av": [], } self.critic = critic_model( obs_dim=self.state_dim, action_dim=action_dim, hidden_dim=400, hidden_depth=2, ).to(self.device) self.critic_target = critic_model( obs_dim=self.state_dim, action_dim=action_dim, hidden_dim=400, hidden_depth=2, ).to(self.device) self.critic_target.load_state_dict(self.critic.state_dict()) self.actor = actor_model( obs_dim=self.state_dim, action_dim=action_dim, hidden_dim=400, hidden_depth=2, log_std_bounds=[-5, 2], ).to(self.device) if load_model: self.load(filename=model_name, directory=load_directory) self.log_alpha = torch.tensor(np.log(init_temperature)).to(self.device) self.log_alpha.requires_grad = True # set target entropy to -|A| self.target_entropy = -action_dim # optimizers self.actor_optimizer = torch.optim.Adam( self.actor.parameters(), lr=actor_lr, betas=actor_betas ) self.critic_optimizer = torch.optim.Adam( self.critic.parameters(), lr=critic_lr, betas=critic_betas ) self.log_alpha_optimizer = torch.optim.Adam( [self.log_alpha], lr=alpha_lr, betas=alpha_betas ) self.critic_target.train() self.actor.train(True) self.critic.train(True) self.step = 0 self.writer = SummaryWriter(comment=model_name) def save(self, filename, directory): """ Save the actor, critic, and target critic models to the specified directory. Args: filename (str): Base name of the saved files. directory (Path): Directory where models are saved. """ Path(directory).mkdir(parents=True, exist_ok=True) torch.save(self.actor.state_dict(), "%s/%s_actor.pth" % (directory, filename)) torch.save(self.critic.state_dict(), "%s/%s_critic.pth" % (directory, filename)) torch.save( self.critic_target.state_dict(), "%s/%s_critic_target.pth" % (directory, filename), ) def load(self, filename, directory): """ Load the actor, critic, and target critic models from the specified directory. Args: filename (str): Base name of the saved files. directory (Path): Directory where models are loaded from. """ self.actor.load_state_dict( torch.load("%s/%s_actor.pth" % (directory, filename)) ) self.critic.load_state_dict( torch.load("%s/%s_critic.pth" % (directory, filename)) ) self.critic_target.load_state_dict( torch.load("%s/%s_critic_target.pth" % (directory, filename)) ) print(f"Loaded weights from: {directory}") def train(self, replay_buffer, iterations, batch_size): """ Run multiple training updates using data from the replay buffer. Args: replay_buffer: Buffer from which to sample training data. iterations (int): Number of training iterations to run. batch_size (int): Batch size for each update. """ for _ in range(iterations): self.update( replay_buffer=replay_buffer, step=self.step, batch_size=batch_size ) for key, value in self.train_metrics_dict.items(): if len(value): self.writer.add_scalar(key, mean(value), self.step) self.train_metrics_dict[key] = [] self.step += 1 if self.save_every > 0 and self.step % self.save_every == 0: self.save(filename=self.model_name, directory=self.save_directory) @property def alpha(self): """ Returns: torch.Tensor: Current value of the entropy temperature alpha. """ return self.log_alpha.exp() def get_action(self, obs, add_noise): """ Select an action given an observation. Args: obs (np.ndarray): Input observation. add_noise (bool): Whether to add exploration noise. Returns: np.ndarray: Action vector. """ if add_noise: return ( self.act(obs) + np.random.normal(0, 0.2, size=self.action_dim) ).clip(self.action_range[0], self.action_range[1]) else: return self.act(obs) def act(self, obs, sample=False): """ Generate an action from the actor network. Args: obs (np.ndarray): Input observation. sample (bool): If True, sample from the policy; otherwise use the mean. Returns: np.ndarray: Action vector. """ obs = torch.FloatTensor(obs).to(self.device) obs = obs.unsqueeze(0) dist = self.actor(obs) action = dist.sample() if sample else dist.mean action = action.clamp(*self.action_range) assert action.ndim == 2 and action.shape[0] == 1 return utils.to_np(action[0]) def update_critic(self, obs, action, reward, next_obs, done, step): """ Update the critic network based on a batch of transitions. Args: obs (torch.Tensor): Batch of current observations. action (torch.Tensor): Batch of actions taken. reward (torch.Tensor): Batch of received rewards. next_obs (torch.Tensor): Batch of next observations. done (torch.Tensor): Batch of done flags. step (int): Current training step (for logging). """ dist = self.actor(next_obs) next_action = dist.rsample() log_prob = dist.log_prob(next_action).sum(-1, keepdim=True) target_Q1, target_Q2 = self.critic_target(next_obs, next_action) target_V = torch.min(target_Q1, target_Q2) - self.alpha.detach() * log_prob target_Q = reward + ((1 - done) * self.discount * target_V) target_Q = target_Q.detach() # get current Q estimates current_Q1, current_Q2 = self.critic(obs, action) critic_loss = F.mse_loss(current_Q1, target_Q) + F.mse_loss( current_Q2, target_Q ) self.train_metrics_dict["train_critic/loss_av"].append(critic_loss.item()) self.writer.add_scalar("train_critic/loss", critic_loss, step) # Optimize the critic self.critic_optimizer.zero_grad() critic_loss.backward() self.critic_optimizer.step() if self.log_dist_and_hist: self.critic.log(self.writer, step) def update_actor_and_alpha(self, obs, step): """ Update the actor and optionally the entropy temperature. Args: obs (torch.Tensor): Batch of observations. step (int): Current training step (for logging). """ dist = self.actor(obs) action = dist.rsample() log_prob = dist.log_prob(action).sum(-1, keepdim=True) actor_Q1, actor_Q2 = self.critic(obs, action) actor_Q = torch.min(actor_Q1, actor_Q2) actor_loss = (self.alpha.detach() * log_prob - actor_Q).mean() self.train_metrics_dict["train_actor/loss_av"].append(actor_loss.item()) self.train_metrics_dict["train_actor/target_entropy_av"].append( self.target_entropy ) self.train_metrics_dict["train_actor/entropy_av"].append( -log_prob.mean().item() ) self.writer.add_scalar("train_actor/loss", actor_loss, step) self.writer.add_scalar("train_actor/target_entropy", self.target_entropy, step) self.writer.add_scalar("train_actor/entropy", -log_prob.mean(), step) # optimize the actor self.actor_optimizer.zero_grad() actor_loss.backward() self.actor_optimizer.step() if self.log_dist_and_hist: self.actor.log(self.writer, step) if self.learnable_temperature: self.log_alpha_optimizer.zero_grad() alpha_loss = ( self.alpha * (-log_prob - self.target_entropy).detach() ).mean() self.train_metrics_dict["train_alpha/loss_av"].append(alpha_loss.item()) self.train_metrics_dict["train_alpha/value_av"].append(self.alpha.item()) self.writer.add_scalar("train_alpha/loss", alpha_loss, step) self.writer.add_scalar("train_alpha/value", self.alpha, step) alpha_loss.backward() self.log_alpha_optimizer.step() def update(self, replay_buffer, step, batch_size): """ Perform a full update step (critic, actor, alpha, target critic). Args: replay_buffer: Buffer to sample from. step (int): Current training step. batch_size (int): Size of sample batch. """ ( batch_states, batch_actions, batch_rewards, batch_dones, batch_next_states, ) = replay_buffer.sample_batch(batch_size) state = torch.Tensor(batch_states).to(self.device) next_state = torch.Tensor(batch_next_states).to(self.device) action = torch.Tensor(batch_actions).to(self.device) reward = torch.Tensor(batch_rewards).to(self.device).reshape(-1, 1) done = torch.Tensor(batch_dones).to(self.device).reshape(-1, 1) self.train_metrics_dict["train/batch_reward_av"].append( batch_rewards.mean().item() ) self.writer.add_scalar("train/batch_reward", batch_rewards.mean(), step) self.update_critic(state, action, reward, next_state, done, step) if step % self.actor_update_frequency == 0: self.update_actor_and_alpha(state, step) if step % self.critic_target_update_frequency == 0: utils.soft_update_params(self.critic, self.critic_target, self.critic_tau) def prepare_state(self, latest_scan, distance, cos, sin, collision, goal, action): """ Convert raw sensor input into a normalized state vector. Args: latest_scan (list or np.ndarray): Laser scan distances. distance (float): Distance to goal. cos (float): Cosine of heading angle to goal. sin (float): Sine of heading angle to goal. collision (bool): Whether the robot has collided. goal (bool): Whether the goal has been reached. action (list): Last action taken [linear_vel, angular_vel]. Returns: tuple: (state vector as list, terminal flag as int) """ latest_scan = np.array(latest_scan) inf_mask = np.isinf(latest_scan) latest_scan[inf_mask] = 7.0 max_bins = self.state_dim - 5 bin_size = int(np.ceil(len(latest_scan) / max_bins)) # Initialize the list to store the minimum values of each bin min_values = [] # Loop through the data and create bins for i in range(0, len(latest_scan), bin_size): # Get the current bin bin = latest_scan[i : i + min(bin_size, len(latest_scan) - i)] # Find the minimum value in the current bin and append it to the min_values list min_values.append(min(bin) / 7) # Normalize to [0, 1] range distance /= 10 lin_vel = action[0] * 2 ang_vel = (action[1] + 1) / 2 state = min_values + [distance, cos, sin] + [lin_vel, ang_vel] assert len(state) == self.state_dim terminal = 1 if collision or goal else 0 return state, terminal ================================================ FILE: robot_nav/models/SAC/SAC_actor.py ================================================ import torch import math from torch import nn import torch.nn.functional as F from torch import distributions as pyd import robot_nav.models.SAC.SAC_utils as utils class TanhTransform(pyd.transforms.Transform): """ A bijective transformation that applies the hyperbolic tangent function. This is used to squash the output of a normal distribution to be within [-1, 1], making it suitable for bounded continuous action spaces. Attributes: domain: The input domain (real numbers). codomain: The output codomain (interval between -1 and 1). bijective: Whether the transform is bijective (True). sign: The sign of the Jacobian determinant (positive). """ domain = pyd.constraints.real codomain = pyd.constraints.interval(-1.0, 1.0) bijective = True sign = +1 def __init__(self, cache_size=1): """ Initialize the TanhTransform. Args: cache_size (int): Size of the cache for storing intermediate values. """ super().__init__(cache_size=cache_size) @staticmethod def atanh(x): """ Inverse hyperbolic tangent function. Args: x (Tensor): Input tensor. Returns: Tensor: atanh(x) """ return 0.5 * (x.log1p() - (-x).log1p()) def __eq__(self, other): """ Equality check for the transform. Returns: bool: True if the other object is also a TanhTransform. """ return isinstance(other, TanhTransform) def _call(self, x): """ Forward transformation. Args: x (Tensor): Input tensor. Returns: Tensor: tanh(x) """ return x.tanh() def _inverse(self, y): """ Inverse transformation. Args: y (Tensor): Input tensor in [-1, 1]. Returns: Tensor: atanh(y) """ # We do not clamp to the boundary here as it may degrade the performance of certain algorithms. # one should use `cache_size=1` instead return self.atanh(y) def log_abs_det_jacobian(self, x, y): """ Log absolute determinant of the Jacobian of the transformation. Args: x (Tensor): Input tensor. y (Tensor): Output tensor. Returns: Tensor: log|det(Jacobian)| """ # We use a formula that is more numerically stable, see details in the following link # https://github.com/tensorflow/probability/commit/ef6bb176e0ebd1cf6e25c6b5cecdd2428c22963f#diff-e120f70e92e6741bca649f04fcd907b7 return 2.0 * (math.log(2.0) - x - F.softplus(-2.0 * x)) class SquashedNormal(pyd.transformed_distribution.TransformedDistribution): """ A squashed (tanh-transformed) diagonal Gaussian distribution. This is used for stochastic policies where actions must be within bounded intervals. """ def __init__(self, loc, scale): """ Initialize the squashed normal distribution. Args: loc (Tensor): Mean of the Gaussian. scale (Tensor): Standard deviation of the Gaussian. """ self.loc = loc self.scale = scale self.base_dist = pyd.Normal(loc, scale) transforms = [TanhTransform()] super().__init__(self.base_dist, transforms) @property def mean(self): """ Compute the mean of the transformed distribution. Returns: Tensor: Mean of the squashed distribution. """ mu = self.loc for tr in self.transforms: mu = tr(mu) return mu class DiagGaussianActor(nn.Module): """ Diagonal Gaussian policy network with tanh squashing. This network outputs a squashed Gaussian distribution given an observation, suitable for continuous control tasks. Args: obs_dim (int): Dimension of the observation space. action_dim (int): Dimension of the action space. hidden_dim (int): Number of units in hidden layers. hidden_depth (int): Number of hidden layers. log_std_bounds (list): Min and max bounds for log standard deviation. """ def __init__(self, obs_dim, action_dim, hidden_dim, hidden_depth, log_std_bounds): """ Initialize the actor network. """ super().__init__() self.log_std_bounds = log_std_bounds self.trunk = utils.mlp(obs_dim, hidden_dim, 2 * action_dim, hidden_depth) self.outputs = dict() self.apply(utils.weight_init) def forward(self, obs): """ Forward pass through the network. Args: obs (Tensor): Observation input. Returns: SquashedNormal: Action distribution with mean and std tracked in `self.outputs`. """ mu, log_std = self.trunk(obs).chunk(2, dim=-1) # constrain log_std inside [log_std_min, log_std_max] log_std = torch.tanh(log_std) log_std_min, log_std_max = self.log_std_bounds log_std = log_std_min + 0.5 * (log_std_max - log_std_min) * (log_std + 1) std = log_std.exp() self.outputs["mu"] = mu self.outputs["std"] = std dist = SquashedNormal(mu, std) return dist def log(self, writer, step): """ Log network outputs (mu and std histograms) to TensorBoard. Args: writer (SummaryWriter): TensorBoard writer instance. step (int): Current global training step. """ for k, v in self.outputs.items(): writer.add_histogram(f"train_actor/{k}_hist", v, step) ================================================ FILE: robot_nav/models/SAC/SAC_critic.py ================================================ import torch from torch import nn import robot_nav.models.SAC.SAC_utils as utils class DoubleQCritic(nn.Module): """ Double Q-learning critic network. Implements two independent Q-functions (Q1 and Q2) to mitigate overestimation bias in value estimates, as introduced in the Twin Delayed Deep Deterministic Policy Gradient (TD3) and Soft Actor-Critic (SAC) algorithms. Args: obs_dim (int): Dimension of the observation space. action_dim (int): Dimension of the action space. hidden_dim (int): Number of units in each hidden layer. hidden_depth (int): Number of hidden layers. """ def __init__(self, obs_dim, action_dim, hidden_dim, hidden_depth): """ Initialize the Double Q-critic network with two MLPs. Q1 and Q2 share the same architecture but have separate weights. """ super().__init__() self.Q1 = utils.mlp(obs_dim + action_dim, hidden_dim, 1, hidden_depth) self.Q2 = utils.mlp(obs_dim + action_dim, hidden_dim, 1, hidden_depth) self.outputs = dict() self.apply(utils.weight_init) def forward(self, obs, action): """ Compute Q-values for the given observation-action pairs. Args: obs (Tensor): Observations of shape (batch_size, obs_dim). action (Tensor): Actions of shape (batch_size, action_dim). Returns: Tuple[Tensor, Tensor]: Q1 and Q2 values, each of shape (batch_size, 1). """ assert obs.size(0) == action.size(0) obs_action = torch.cat([obs, action], dim=-1) q1 = self.Q1(obs_action) q2 = self.Q2(obs_action) self.outputs["q1"] = q1 self.outputs["q2"] = q2 return q1, q2 def log(self, writer, step): """ Log histograms of Q-value distributions to TensorBoard. Args: writer (SummaryWriter): TensorBoard writer instance. step (int): Current training step (global). """ for k, v in self.outputs.items(): writer.add_histogram(f"train_critic/{k}_hist", v, step) ================================================ FILE: robot_nav/models/SAC/SAC_utils.py ================================================ import numpy as np import torch from torch import nn from torch import distributions as pyd import torch.nn.functional as F import os from collections import deque import random import math def soft_update_params(net, target_net, tau): """ Perform a soft update of the parameters of the target network. Args: net (nn.Module): Source network whose parameters are used for updating. target_net (nn.Module): Target network to be updated. tau (float): Interpolation parameter (0 < tau < 1) for soft updates. A value closer to 1 means faster updates. """ for param, target_param in zip(net.parameters(), target_net.parameters()): target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data) def set_seed_everywhere(seed): """ Set random seed for reproducibility across NumPy, random, and PyTorch. Args: seed (int): Random seed. """ torch.manual_seed(seed) if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) np.random.seed(seed) random.seed(seed) def make_dir(*path_parts): """ Create a directory if it does not exist. Args: *path_parts (str): Components of the path to be joined into the directory. Returns: str: The full path of the created or existing directory. """ dir_path = os.path.join(*path_parts) try: os.mkdir(dir_path) except OSError: pass return dir_path def weight_init(m): """ Custom weight initialization for layers. Applies orthogonal initialization to Linear layers and zero initialization to biases. Args: m (nn.Module): Layer to initialize. """ if isinstance(m, nn.Linear): nn.init.orthogonal_(m.weight.data) if hasattr(m.bias, "data"): m.bias.data.fill_(0.0) class MLP(nn.Module): """ Multi-layer perceptron (MLP) with configurable depth and optional output activation. Args: input_dim (int): Number of input features. hidden_dim (int): Number of hidden units in each hidden layer. output_dim (int): Number of output features. hidden_depth (int): Number of hidden layers. output_mod (nn.Module, optional): Optional output activation module (e.g., Tanh, Sigmoid). """ def __init__( self, input_dim, hidden_dim, output_dim, hidden_depth, output_mod=None ): super().__init__() self.trunk = mlp(input_dim, hidden_dim, output_dim, hidden_depth, output_mod) self.apply(weight_init) def forward(self, x): """ Forward pass through the MLP. Args: x (Tensor): Input tensor of shape (batch_size, input_dim). Returns: Tensor: Output tensor of shape (batch_size, output_dim). """ return self.trunk(x) def mlp(input_dim, hidden_dim, output_dim, hidden_depth, output_mod=None): """ Create an MLP as a `nn.Sequential` module. Args: input_dim (int): Input feature dimension. hidden_dim (int): Hidden layer size. output_dim (int): Output feature dimension. hidden_depth (int): Number of hidden layers. output_mod (nn.Module, optional): Output activation module. Returns: nn.Sequential: The constructed MLP. """ if hidden_depth == 0: mods = [nn.Linear(input_dim, output_dim)] else: mods = [nn.Linear(input_dim, hidden_dim), nn.ReLU(inplace=True)] for i in range(hidden_depth - 1): mods += [nn.Linear(hidden_dim, hidden_dim), nn.ReLU(inplace=True)] mods.append(nn.Linear(hidden_dim, output_dim)) if output_mod is not None: mods.append(output_mod) trunk = nn.Sequential(*mods) return trunk def to_np(t): if t is None: return None elif t.nelement() == 0: return np.array([]) else: return t.cpu().detach().numpy() ================================================ FILE: robot_nav/models/SAC/__init__.py ================================================ ================================================ FILE: robot_nav/models/TD3/TD3.py ================================================ from pathlib import Path import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from numpy import inf from torch.utils.tensorboard import SummaryWriter from robot_nav.utils import get_max_bound class Actor(nn.Module): """ Actor network for the TD3 algorithm. This neural network maps states to actions using a feedforward architecture with LeakyReLU activations and a final Tanh output to bound the actions in [-1, 1]. Architecture: Input: state_dim Hidden Layer 1: 400 units, LeakyReLU Hidden Layer 2: 300 units, LeakyReLU Output Layer: action_dim, Tanh Args: state_dim (int): Dimension of the input state. action_dim (int): Dimension of the action output. """ def __init__(self, state_dim, action_dim): super(Actor, self).__init__() self.layer_1 = nn.Linear(state_dim, 400) torch.nn.init.kaiming_uniform_(self.layer_1.weight, nonlinearity="leaky_relu") self.layer_2 = nn.Linear(400, 300) torch.nn.init.kaiming_uniform_(self.layer_2.weight, nonlinearity="leaky_relu") self.layer_3 = nn.Linear(300, action_dim) self.tanh = nn.Tanh() def forward(self, s): """ Perform a forward pass through the actor network. Args: s (torch.Tensor): Input state tensor. Returns: (torch.Tensor): Action output tensor after Tanh activation. """ s = F.leaky_relu(self.layer_1(s)) s = F.leaky_relu(self.layer_2(s)) a = self.tanh(self.layer_3(s)) return a class Critic(nn.Module): """ Critic network for the TD3 algorithm. This class defines two Q-value estimators (Q1 and Q2) using separate subnetworks. Each Q-network takes both state and action as input and outputs a scalar Q-value. Architecture for each Q-network: Input: state_dim and action_dim - State pathway: Linear + LeakyReLU → 400 → 300 - Action pathway: Linear → 300 - Combined pathway: LeakyReLU(Linear(state) + Linear(action) + bias) → 1 Args: state_dim (int): Dimension of the input state. action_dim (int): Dimension of the input action. """ def __init__(self, state_dim, action_dim): super(Critic, self).__init__() self.layer_1 = nn.Linear(state_dim, 400) torch.nn.init.kaiming_uniform_(self.layer_1.weight, nonlinearity="leaky_relu") self.layer_2_s = nn.Linear(400, 300) torch.nn.init.kaiming_uniform_(self.layer_2_s.weight, nonlinearity="leaky_relu") self.layer_2_a = nn.Linear(action_dim, 300) torch.nn.init.kaiming_uniform_(self.layer_2_a.weight, nonlinearity="leaky_relu") self.layer_3 = nn.Linear(300, 1) torch.nn.init.kaiming_uniform_(self.layer_3.weight, nonlinearity="leaky_relu") self.layer_4 = nn.Linear(state_dim, 400) torch.nn.init.kaiming_uniform_(self.layer_1.weight, nonlinearity="leaky_relu") self.layer_5_s = nn.Linear(400, 300) torch.nn.init.kaiming_uniform_(self.layer_5_s.weight, nonlinearity="leaky_relu") self.layer_5_a = nn.Linear(action_dim, 300) torch.nn.init.kaiming_uniform_(self.layer_5_a.weight, nonlinearity="leaky_relu") self.layer_6 = nn.Linear(300, 1) torch.nn.init.kaiming_uniform_(self.layer_6.weight, nonlinearity="leaky_relu") def forward(self, s, a): """ Perform a forward pass through both Q-networks. Args: s (torch.Tensor): Input state tensor. a (torch.Tensor): Input action tensor. Returns: (tuple): - q1 (torch.Tensor): Output Q-value from the first critic network. - q2 (torch.Tensor): Output Q-value from the second critic network. """ s1 = F.leaky_relu(self.layer_1(s)) self.layer_2_s(s1) self.layer_2_a(a) s11 = torch.mm(s1, self.layer_2_s.weight.data.t()) s12 = torch.mm(a, self.layer_2_a.weight.data.t()) s1 = F.leaky_relu(s11 + s12 + self.layer_2_a.bias.data) q1 = self.layer_3(s1) s2 = F.leaky_relu(self.layer_4(s)) self.layer_5_s(s2) self.layer_5_a(a) s21 = torch.mm(s2, self.layer_5_s.weight.data.t()) s22 = torch.mm(a, self.layer_5_a.weight.data.t()) s2 = F.leaky_relu(s21 + s22 + self.layer_5_a.bias.data) q2 = self.layer_6(s2) return q1, q2 # TD3 network class TD3(object): def __init__( self, state_dim, action_dim, max_action, device, lr=1e-4, save_every=0, load_model=False, save_directory=Path("robot_nav/models/TD3/checkpoint"), model_name="TD3", load_directory=Path("robot_nav/models/TD3/checkpoint"), use_max_bound=False, bound_weight=0.25, ): """ Twin Delayed Deep Deterministic Policy Gradient (TD3) agent. This class implements the TD3 reinforcement learning algorithm for continuous control. It uses an Actor-Critic architecture with target networks and delayed policy updates. Args: state_dim (int): Dimension of the input state. action_dim (int): Dimension of the action space. max_action (float): Maximum allowed value for actions. device (torch.device): Device to run the model on (CPU or CUDA). lr (float, optional): Learning rate for both actor and critic. Default is 1e-4. save_every (int, optional): Save model every `save_every` iterations. Default is 0. load_model (bool, optional): Whether to load model from checkpoint. Default is False. save_directory (Path, optional): Directory to save model checkpoints. model_name (str, optional): Name to use when saving/loading models. load_directory (Path, optional): Directory to load model checkpoints from. use_max_bound (bool, optional): Whether to apply maximum Q-value bounding during training. bound_weight (float, optional): Weight for the max-bound loss penalty. """ self.device = device # Initialize the Actor network self.actor = Actor(state_dim, action_dim).to(self.device) self.actor_target = Actor(state_dim, action_dim).to(self.device) self.actor_target.load_state_dict(self.actor.state_dict()) self.actor_optimizer = torch.optim.Adam(params=self.actor.parameters(), lr=lr) # Initialize the Critic networks self.critic = Critic(state_dim, action_dim).to(self.device) self.critic_target = Critic(state_dim, action_dim).to(self.device) self.critic_target.load_state_dict(self.critic.state_dict()) self.critic_optimizer = torch.optim.Adam(params=self.critic.parameters(), lr=lr) self.action_dim = action_dim self.max_action = max_action self.state_dim = state_dim self.writer = SummaryWriter(comment=model_name) self.iter_count = 0 if load_model: self.load(filename=model_name, directory=load_directory) self.save_every = save_every self.model_name = model_name self.save_directory = save_directory self.use_max_bound = use_max_bound self.bound_weight = bound_weight def get_action(self, obs, add_noise): """ Get an action from the current policy with optional exploration noise. Args: obs (np.ndarray): The current state observation. add_noise (bool): Whether to add exploration noise. Returns: (np.ndarray): The chosen action clipped to [-max_action, max_action]. """ if add_noise: return ( self.act(obs) + np.random.normal(0, 0.2, size=self.action_dim) ).clip(-self.max_action, self.max_action) else: return self.act(obs) def act(self, state): """ Compute the action using the actor network without exploration noise. Args: state (np.ndarray): The current environment state. Returns: (np.ndarray): The deterministic action predicted by the actor. """ state = torch.Tensor(state).to(self.device) return self.actor(state).cpu().data.numpy().flatten() # training cycle def train( self, replay_buffer, iterations, batch_size, discount=0.99, tau=0.005, policy_noise=0.2, noise_clip=0.5, policy_freq=2, max_lin_vel=0.5, max_ang_vel=1, goal_reward=100, distance_norm=10, time_step=0.3, ): """ Train the TD3 agent using batches sampled from the replay buffer. Args: replay_buffer (ReplayBuffer): The replay buffer to sample experiences from. iterations (int): Number of training iterations to perform. batch_size (int): Size of each mini-batch. discount (float): Discount factor gamma for future rewards. tau (float): Soft update rate for target networks. policy_noise (float): Stddev of Gaussian noise added to target actions. noise_clip (float): Maximum magnitude of noise added to target actions. policy_freq (int): Frequency of policy (actor) updates. max_lin_vel (float): Max linear velocity used for upper bound estimation. max_ang_vel (float): Max angular velocity used for upper bound estimation. goal_reward (float): Reward given for reaching the goal. distance_norm (float): Distance normalization factor. time_step (float): Time step used in upper bound calculations. """ av_Q = 0 max_Q = -inf av_loss = 0 for it in range(iterations): # sample a batch from the replay buffer ( batch_states, batch_actions, batch_rewards, batch_dones, batch_next_states, ) = replay_buffer.sample_batch(batch_size) state = torch.Tensor(batch_states).to(self.device) next_state = torch.Tensor(batch_next_states).to(self.device) action = torch.Tensor(batch_actions).to(self.device) reward = torch.Tensor(batch_rewards).to(self.device).reshape(-1, 1) done = torch.Tensor(batch_dones).to(self.device).reshape(-1, 1) # Obtain the estimated action from the next state by using the actor-target next_action = self.actor_target(next_state) # Add noise to the action noise = ( torch.Tensor(batch_actions) .data.normal_(0, policy_noise) .to(self.device) ) noise = noise.clamp(-noise_clip, noise_clip) next_action = (next_action + noise).clamp(-self.max_action, self.max_action) # Calculate the Q values from the critic-target network for the next state-action pair target_Q1, target_Q2 = self.critic_target(next_state, next_action) # Select the minimal Q value from the 2 calculated values target_Q = torch.min(target_Q1, target_Q2) av_Q += torch.mean(target_Q) max_Q = max(max_Q, torch.max(target_Q)) # Calculate the final Q value from the target network parameters by using Bellman equation target_Q = reward + ((1 - done) * discount * target_Q).detach() # Get the Q values of the basis networks with the current parameters current_Q1, current_Q2 = self.critic(state, action) # Calculate the loss between the current Q value and the target Q value loss = F.mse_loss(current_Q1, target_Q) + F.mse_loss(current_Q2, target_Q) if self.use_max_bound: max_bound = get_max_bound( next_state, discount, max_ang_vel, max_lin_vel, time_step, distance_norm, goal_reward, reward, done, self.device, ) max_excess_Q1 = F.relu(current_Q1 - max_bound) max_excess_Q2 = F.relu(current_Q2 - max_bound) max_bound_loss = (max_excess_Q1**2).mean() + (max_excess_Q2**2).mean() # Add loss for Q values exceeding maximum possible upper bound loss += self.bound_weight * max_bound_loss # Perform the gradient descent self.critic_optimizer.zero_grad() loss.backward() self.critic_optimizer.step() if it % policy_freq == 0: # Maximize the actor output value by performing gradient descent on negative Q values # (essentially perform gradient ascent) actor_grad, _ = self.critic(state, self.actor(state)) actor_grad = -actor_grad.mean() self.actor_optimizer.zero_grad() actor_grad.backward() self.actor_optimizer.step() # Use soft update to update the actor-target network parameters by # infusing small amount of current parameters for param, target_param in zip( self.actor.parameters(), self.actor_target.parameters() ): target_param.data.copy_( tau * param.data + (1 - tau) * target_param.data ) # Use soft update to update the critic-target network parameters by infusing # small amount of current parameters for param, target_param in zip( self.critic.parameters(), self.critic_target.parameters() ): target_param.data.copy_( tau * param.data + (1 - tau) * target_param.data ) av_loss += loss self.iter_count += 1 # Write new values for tensorboard self.writer.add_scalar("train/loss", av_loss / iterations, self.iter_count) self.writer.add_scalar("train/avg_Q", av_Q / iterations, self.iter_count) self.writer.add_scalar("train/max_Q", max_Q, self.iter_count) if self.save_every > 0 and self.iter_count % self.save_every == 0: self.save(filename=self.model_name, directory=self.save_directory) def save(self, filename, directory): """ Save the actor and critic networks (and their targets) to disk. Args: filename (str): Name to use when saving model files. directory (Path): Directory where models should be saved. """ Path(directory).mkdir(parents=True, exist_ok=True) torch.save(self.actor.state_dict(), "%s/%s_actor.pth" % (directory, filename)) torch.save( self.actor_target.state_dict(), "%s/%s_actor_target.pth" % (directory, filename), ) torch.save(self.critic.state_dict(), "%s/%s_critic.pth" % (directory, filename)) torch.save( self.critic_target.state_dict(), "%s/%s_critic_target.pth" % (directory, filename), ) def load(self, filename, directory): """ Load the actor and critic networks (and their targets) from disk. Args: filename (str): Name used when saving the models. directory (Path): Directory where models are saved. """ self.actor.load_state_dict( torch.load("%s/%s_actor.pth" % (directory, filename)) ) self.actor_target.load_state_dict( torch.load("%s/%s_actor_target.pth" % (directory, filename)) ) self.critic.load_state_dict( torch.load("%s/%s_critic.pth" % (directory, filename)) ) self.critic_target.load_state_dict( torch.load("%s/%s_critic_target.pth" % (directory, filename)) ) print(f"Loaded weights from: {directory}") def prepare_state(self, latest_scan, distance, cos, sin, collision, goal, action): """ Prepare the input state vector for training or inference. Combines processed laser scan data, goal vector, and past action into a normalized state input matching the input dimension. Args: latest_scan (list or np.ndarray): Laser scan data. distance (float): Distance to goal. cos (float): Cosine of the heading angle to goal. sin (float): Sine of the heading angle to goal. collision (bool): Whether a collision occurred. goal (bool): Whether the goal has been reached. action (list or np.ndarray): Last executed action [linear_vel, angular_vel]. Returns: (tuple): - state (list): Prepared and normalized state vector. - terminal (int): 1 if episode should terminate (goal or collision), else 0. """ latest_scan = np.array(latest_scan) inf_mask = np.isinf(latest_scan) latest_scan[inf_mask] = 7.0 max_bins = self.state_dim - 5 bin_size = int(np.ceil(len(latest_scan) / max_bins)) # Initialize the list to store the minimum values of each bin min_values = [] # Loop through the data and create bins for i in range(0, len(latest_scan), bin_size): # Get the current bin bin = latest_scan[i : i + min(bin_size, len(latest_scan) - i)] # Find the minimum value in the current bin and append it to the min_values list min_values.append(min(bin) / 7) # Normalize to [0, 1] range distance /= 10 lin_vel = action[0] * 2 ang_vel = (action[1] + 1) / 2 state = min_values + [distance, cos, sin] + [lin_vel, ang_vel] assert len(state) == self.state_dim, f"{len(state), self.state_dim}" terminal = 1 if collision or goal else 0 return state, terminal ================================================ FILE: robot_nav/models/TD3/__init__.py ================================================ ================================================ FILE: robot_nav/models/__init__.py ================================================ ================================================ FILE: robot_nav/replay_buffer.py ================================================ import random from collections import deque import itertools import numpy as np class ReplayBuffer(object): """ Standard experience replay buffer for off-policy reinforcement learning algorithms. Stores tuples of (state, action, reward, done, next_state) up to a fixed capacity, enabling sampling of uncorrelated mini-batches for training. """ def __init__(self, buffer_size, random_seed=123): """ Initialize the replay buffer. Args: buffer_size (int): Maximum number of transitions to store in the buffer. random_seed (int): Seed for random number generation. """ self.buffer_size = buffer_size self.count = 0 self.buffer = deque() random.seed(random_seed) def add(self, s, a, r, t, s2): """ Add a transition to the buffer. Args: s (np.ndarray): State. a (np.ndarray): Action. r (float): Reward. t (bool): Done flag (True if episode ended). s2 (np.ndarray): Next state. """ experience = (s, a, r, t, s2) if self.count < self.buffer_size: self.buffer.append(experience) self.count += 1 else: self.buffer.popleft() self.buffer.append(experience) def size(self): """ Get the number of elements currently in the buffer. Returns: (int): Current buffer size. """ return self.count def sample_batch(self, batch_size): """ Sample a batch of experiences from the buffer. Args: batch_size (int): Number of experiences to sample. Returns: (Tuple of np.ndarrays): Batches of states, actions, rewards, done flags, and next states. """ if self.count < batch_size: batch = random.sample(self.buffer, self.count) else: batch = random.sample(self.buffer, batch_size) s_batch = np.array([_[0] for _ in batch]) a_batch = np.array([_[1] for _ in batch]) r_batch = np.array([_[2] for _ in batch]) t_batch = np.array([_[3] for _ in batch]) s2_batch = np.array([_[4] for _ in batch]) return s_batch, a_batch, r_batch, t_batch, s2_batch def return_buffer(self): """ Return the entire buffer contents as separate arrays. Returns: (Tuple of np.ndarrays): Full arrays of states, actions, rewards, done flags, and next states. """ s = np.array([_[0] for _ in self.buffer]) a = np.array([_[1] for _ in self.buffer]) r = np.array([_[2] for _ in self.buffer]).reshape(-1, 1) t = np.array([_[3] for _ in self.buffer]).reshape(-1, 1) s2 = np.array([_[4] for _ in self.buffer]) return s, a, r, t, s2 def clear(self): """ Clear all contents of the buffer. """ self.buffer.clear() self.count = 0 class RolloutReplayBuffer(object): """ Replay buffer that stores full episode rollouts, allowing access to historical trajectories. Useful for algorithms that condition on sequences of past states (e.g., RNN-based policies). """ def __init__(self, buffer_size, random_seed=123, history_len=10): """ Initialize the rollout replay buffer. Args: buffer_size (int): Maximum number of episodes (rollouts) to store. random_seed (int): Seed for random number generation. history_len (int): Number of past steps to return for each sampled state. """ self.buffer_size = buffer_size self.count = 0 self.buffer = deque(maxlen=buffer_size) random.seed(random_seed) self.buffer.append([]) self.history_len = history_len def add(self, s, a, r, t, s2): """ Add a transition to the current episode. If the transition ends the episode (t=True), a new episode is started. Args: s (np.ndarray): State. a (np.ndarray): Action. r (float): Reward. t (bool): Done flag. s2 (np.ndarray): Next state. """ experience = (s, a, r, t, s2) if t: self.count += 1 self.buffer[-1].append(experience) self.buffer.append([]) else: self.buffer[-1].append(experience) def size(self): """ Get the number of complete episodes in the buffer. Returns: (int): Number of episodes. """ return self.count def sample_batch(self, batch_size): """ Sample a batch of state sequences and corresponding transitions from full episodes. Returns past `history_len` steps for each sampled transition, padded with the earliest step if necessary. Args: batch_size (int): Number of sequences to sample. Returns: (Tuple of np.ndarrays): Sequences of past states, actions, rewards, done flags, and next states. """ if self.count < batch_size: batch = random.sample( list(itertools.islice(self.buffer, 0, len(self.buffer) - 1)), self.count ) else: batch = random.sample( list(itertools.islice(self.buffer, 0, len(self.buffer) - 1)), batch_size ) idx = [random.randint(0, len(b) - 1) for b in batch] s_batch = [] s2_batch = [] for i in range(len(batch)): if idx[i] == len(batch[i]): s = batch[i] s2 = batch[i] else: s = batch[i][: idx[i] + 1] s2 = batch[i][: idx[i] + 1] s = [v[0] for v in s] s = s[::-1] s2 = [v[4] for v in s2] s2 = s2[::-1] if len(s) < self.history_len: missing = self.history_len - len(s) s += [s[-1]] * missing s2 += [s2[-1]] * missing else: s = s[: self.history_len] s2 = s2[: self.history_len] s = s[::-1] s_batch.append(s) s2 = s2[::-1] s2_batch.append(s2) a_batch = np.array([batch[i][idx[i]][1] for i in range(len(batch))]) r_batch = np.array([batch[i][idx[i]][2] for i in range(len(batch))]).reshape( -1, 1 ) t_batch = np.array([batch[i][idx[i]][3] for i in range(len(batch))]).reshape( -1, 1 ) return np.array(s_batch), a_batch, r_batch, t_batch, np.array(s2_batch) def clear(self): """ Clear all stored episodes from the buffer. """ self.buffer.clear() self.count = 0 ================================================ FILE: robot_nav/rl_test.py ================================================ from models.TD3.TD3 import TD3 import torch import numpy as np from robot_nav.SIM_ENV.sim import SIM import yaml def main(args=None): """Main testing function""" action_dim = 2 # number of actions produced by the model max_action = 1 # maximum absolute value of output actions state_dim = 25 # number of input values in the neural network (vector length of state input) device = torch.device( "cuda" if torch.cuda.is_available() else "cpu" ) # using cuda if it is available, cpu otherwise epoch = 0 # epoch number max_steps = 300 # maximum number of steps in single episode model = TD3( state_dim=state_dim, action_dim=action_dim, max_action=max_action, device=device, load_model=True, model_name="TD3", ) # instantiate a model sim = SIM(world_file="worlds/eval_world.yaml") # instantiate environment with open("robot_nav/eval_points.yaml") as file: points = yaml.safe_load(file) robot_poses = points["robot"]["poses"] robot_goals = points["robot"]["goals"] assert len(robot_poses) == len( robot_goals ), "Number of robot poses do not equal the robot goals" print("..............................................") print(f"Testing {len(robot_poses)} scenarios") total_reward = 0.0 total_steps = 0 col = 0 goals = 0 for idx in range(len(robot_poses)): count = 0 latest_scan, distance, cos, sin, collision, goal, a, reward = sim.reset( robot_state=robot_poses[idx], robot_goal=robot_goals[idx], random_obstacles=False, ) done = False while not done and count < max_steps: state, terminal = model.prepare_state( latest_scan, distance, cos, sin, collision, goal, a ) action = model.get_action(np.array(state), False) a_in = [(action[0] + 1) / 4, action[1]] latest_scan, distance, cos, sin, collision, goal, a, reward = sim.step( lin_velocity=a_in[0], ang_velocity=a_in[1] ) total_reward += reward total_steps += 1 count += 1 if collision: col += 1 if goal: goals += 1 done = collision or goal avg_step_reward = total_reward / total_steps avg_reward = total_reward / len(robot_poses) avg_col = col / len(robot_poses) avg_goal = goals / len(robot_poses) print(f"Total Reward: {total_reward}") print(f"Average Reward: {avg_reward}") print(f"Average Step Reward: {avg_step_reward}") print(f"Average Collision rate: {avg_col}") print(f"Average Goal rate: {avg_goal}") print("..............................................") model.writer.add_scalar("test/total_reward", total_reward, epoch) model.writer.add_scalar("test/avg_reward", avg_reward, epoch) model.writer.add_scalar("test/avg_step_reward", avg_step_reward, epoch) model.writer.add_scalar("test/avg_col", avg_col, epoch) model.writer.add_scalar("test/avg_goal", avg_goal, epoch) if __name__ == "__main__": main() ================================================ FILE: robot_nav/rl_test_random.py ================================================ from robot_nav.models.SAC.SAC import SAC import statistics import numpy as np import tqdm import matplotlib.pyplot as plt import torch from robot_nav.SIM_ENV.sim import SIM def main(args=None): """Main testing function""" action_dim = 2 # number of actions produced by the model max_action = 1 # maximum absolute value of output actions state_dim = 25 # number of input values in the neural network (vector length of state input) device = torch.device( "cuda" if torch.cuda.is_available() else "cpu" ) # using cuda if it is available, cpu otherwise epoch = 0 # epoch number max_steps = 300 # maximum number of steps in single episode test_scenarios = 1000 model = SAC( state_dim=state_dim, action_dim=action_dim, max_action=max_action, device=device, load_model=True, model_name="SAC", ) # instantiate a model sim = SIM( world_file="worlds/eval_world.yaml", disable_plotting=False ) # instantiate environment print("..............................................") print(f"Testing {test_scenarios} scenarios") total_reward = [] reward_per_ep = [] lin_actions = [] ang_actions = [] total_steps = 0 col = 0 goals = 0 inter_rew = [] steps_to_goal = [] for _ in tqdm.tqdm(range(test_scenarios)): count = 0 ep_reward = 0 latest_scan, distance, cos, sin, collision, goal, a, reward = sim.reset( robot_state=None, robot_goal=None, random_obstacles=True, random_obstacle_ids=[i + 1 for i in range(6)], ) done = False while not done and count < max_steps: state, terminal = model.prepare_state( latest_scan, distance, cos, sin, collision, goal, a ) action = model.get_action(np.array(state), False) a_in = [(action[0] + 1) / 4, action[1]] lin_actions.append(a_in[0]) ang_actions.append(a_in[1]) latest_scan, distance, cos, sin, collision, goal, a, reward = sim.step( lin_velocity=a_in[0], ang_velocity=a_in[1] ) ep_reward += reward total_reward.append(reward) total_steps += 1 count += 1 if collision: col += 1 if goal: goals += 1 steps_to_goal.append(count) done = collision or goal if done: reward_per_ep.append(ep_reward) if not done: inter_rew.append(reward) total_reward = np.array(total_reward) reward_per_ep = np.array(reward_per_ep) inter_rew = np.array(inter_rew) steps_to_goal = np.array(steps_to_goal) lin_actions = np.array(lin_actions) ang_actions = np.array(ang_actions) avg_step_reward = statistics.mean(total_reward) avg_step_reward_std = statistics.stdev(total_reward) avg_ep_reward = statistics.mean(reward_per_ep) avg_ep_reward_std = statistics.stdev(reward_per_ep) avg_col = col / test_scenarios avg_goal = goals / test_scenarios avg_inter_step_rew = statistics.mean(inter_rew) avg_inter_step_rew_std = statistics.stdev(inter_rew) avg_steps_to_goal = statistics.mean(steps_to_goal) avg_steps_to_goal_std = statistics.stdev(steps_to_goal) mean_lin_action = statistics.mean(lin_actions) lin_actions_std = statistics.stdev(lin_actions) mean_ang_action = statistics.mean(ang_actions) ang_actions_std = statistics.stdev(ang_actions) print(f"avg_step_reward {avg_step_reward}") print(f"avg_step_reward_std: {avg_step_reward_std}") print(f"avg_ep_reward: {avg_ep_reward}") print(f"avg_ep_reward_std: {avg_ep_reward_std}") print(f"avg_col: {avg_col}") print(f"avg_goal: {avg_goal}") print(f"avg_inter_step_rew: {avg_inter_step_rew}") print(f"avg_inter_step_rew_std: {avg_inter_step_rew_std}") print(f"avg_steps_to_goal: {avg_steps_to_goal}") print(f"avg_steps_to_goal_std: {avg_steps_to_goal_std}") print(f"mean_lin_action: {mean_lin_action}") print(f"lin_actions_std: {lin_actions_std}") print(f"mean_ang_action: {mean_ang_action}") print(f"ang_actions_std: {ang_actions_std}") print("..............................................") model.writer.add_scalar("test/avg_step_reward", avg_step_reward, epoch) model.writer.add_scalar("test/avg_step_reward_std", avg_step_reward_std, epoch) model.writer.add_scalar("test/avg_ep_reward", avg_ep_reward, epoch) model.writer.add_scalar("test/avg_ep_reward_std", avg_ep_reward_std, epoch) model.writer.add_scalar("test/avg_col", avg_col, epoch) model.writer.add_scalar("test/avg_goal", avg_goal, epoch) model.writer.add_scalar("test/avg_inter_step_rew", avg_inter_step_rew, epoch) model.writer.add_scalar( "test/avg_inter_step_rew_std", avg_inter_step_rew_std, epoch ) model.writer.add_scalar("test/avg_steps_to_goal", avg_steps_to_goal, epoch) model.writer.add_scalar("test/avg_steps_to_goal_std", avg_steps_to_goal_std, epoch) model.writer.add_scalar("test/mean_lin_action", mean_lin_action, epoch) model.writer.add_scalar("test/lin_actions_std", lin_actions_std, epoch) model.writer.add_scalar("test/mean_ang_action", mean_ang_action, epoch) model.writer.add_scalar("test/ang_actions_std", ang_actions_std, epoch) bins = 100 model.writer.add_histogram("test/lin_actions", lin_actions, epoch, max_bins=bins) model.writer.add_histogram("test/ang_actions", ang_actions, epoch, max_bins=bins) counts, bin_edges = np.histogram(lin_actions, bins=bins) fig, ax = plt.subplots() ax.bar( bin_edges[:-1], counts, width=np.diff(bin_edges), align="edge", log=True ) # Log scale on y-axis ax.set_xlabel("Value") ax.set_ylabel("Frequency (Log Scale)") ax.set_title("Histogram with Log Scale") model.writer.add_figure("test/lin_actions_hist", fig) counts, bin_edges = np.histogram(ang_actions, bins=bins) fig, ax = plt.subplots() ax.bar( bin_edges[:-1], counts, width=np.diff(bin_edges), align="edge", log=True ) # Log scale on y-axis ax.set_xlabel("Value") ax.set_ylabel("Frequency (Log Scale)") ax.set_title("Histogram with Log Scale") model.writer.add_figure("test/ang_actions_hist", fig) if __name__ == "__main__": main() ================================================ FILE: robot_nav/rl_train.py ================================================ from robot_nav.models.CNNTD3.CNNTD3 import CNNTD3 import torch import numpy as np from robot_nav.SIM_ENV.sim import SIM from utils import get_buffer def main(args=None): """Main training function""" action_dim = 2 # number of actions produced by the model max_action = 1 # maximum absolute value of output actions state_dim = 185 # number of input values in the neural network (vector length of state input) device = torch.device( "cuda" if torch.cuda.is_available() else "cpu" ) # using cuda if it is available, cpu otherwise nr_eval_episodes = 10 # how many episodes to use to run evaluation max_epochs = 60 # max number of epochs epoch = 0 # starting epoch number episodes_per_epoch = 70 # how many episodes to run in single epoch episode = 0 # starting episode number train_every_n = 2 # train and update network parameters every n episodes training_iterations = 80 # how many batches to use for single training cycle batch_size = 64 # batch size for each training iteration max_steps = 300 # maximum number of steps in single episode steps = 0 # starting step number load_saved_buffer = False # whether to load experiences from assets/data.yml pretrain = False # whether to use the loaded experiences to pre-train the model (load_saved_buffer must be True) pretraining_iterations = ( 10 # number of training iterations to run during pre-training ) save_every = 5 # save the model every n training cycles model = CNNTD3( state_dim=state_dim, action_dim=action_dim, max_action=max_action, device=device, save_every=save_every, load_model=False, model_name="CNNTD3", ) # instantiate a model sim = SIM( world_file="worlds/robot_world.yaml", disable_plotting=False ) # instantiate environment replay_buffer = get_buffer( model, sim, load_saved_buffer, pretrain, pretraining_iterations, training_iterations, batch_size, ) latest_scan, distance, cos, sin, collision, goal, a, reward = sim.step( lin_velocity=0.0, ang_velocity=0.0 ) # get the initial step state while epoch < max_epochs: # train until max_epochs is reached state, terminal = model.prepare_state( latest_scan, distance, cos, sin, collision, goal, a ) # get state a state representation from returned data from the environment action = model.get_action(np.array(state), True) # get an action from the model a_in = [ (action[0] + 1) / 4, action[1], ] # clip linear velocity to [0, 0.5] m/s range latest_scan, distance, cos, sin, collision, goal, a, reward = sim.step( lin_velocity=a_in[0], ang_velocity=a_in[1] ) # get data from the environment next_state, terminal = model.prepare_state( latest_scan, distance, cos, sin, collision, goal, a ) # get a next state representation replay_buffer.add( state, action, reward, terminal, next_state ) # add experience to the replay buffer if ( terminal or steps == max_steps ): # reset environment of terminal stat ereached, or max_steps were taken latest_scan, distance, cos, sin, collision, goal, a, reward = sim.reset() episode += 1 if episode % train_every_n == 0: model.train( replay_buffer=replay_buffer, iterations=training_iterations, batch_size=batch_size, ) # train the model and update its parameters steps = 0 else: steps += 1 if ( episode + 1 ) % episodes_per_epoch == 0: # if epoch is concluded, run evaluation episode = 0 epoch += 1 evaluate(model, epoch, sim, eval_episodes=nr_eval_episodes) def evaluate(model, epoch, sim, eval_episodes=10): print("..............................................") print(f"Epoch {epoch}. Evaluating scenarios") avg_reward = 0.0 col = 0 goals = 0 for _ in range(eval_episodes): count = 0 latest_scan, distance, cos, sin, collision, goal, a, reward = sim.reset() done = False while not done and count < 501: state, terminal = model.prepare_state( latest_scan, distance, cos, sin, collision, goal, a ) action = model.get_action(np.array(state), False) a_in = [(action[0] + 1) / 4, action[1]] latest_scan, distance, cos, sin, collision, goal, a, reward = sim.step( lin_velocity=a_in[0], ang_velocity=a_in[1] ) avg_reward += reward count += 1 if collision: col += 1 if goal: goals += 1 done = collision or goal avg_reward /= eval_episodes avg_col = col / eval_episodes avg_goal = goals / eval_episodes print(f"Average Reward: {avg_reward}") print(f"Average Collision rate: {avg_col}") print(f"Average Goal rate: {avg_goal}") print("..............................................") model.writer.add_scalar("eval/avg_reward", avg_reward, epoch) model.writer.add_scalar("eval/avg_col", avg_col, epoch) model.writer.add_scalar("eval/avg_goal", avg_goal, epoch) if __name__ == "__main__": main() ================================================ FILE: robot_nav/rnn_test.py ================================================ from collections import deque from robot_nav.models.RCPG.RCPG import RCPG import torch import numpy as np from robot_nav.SIM_ENV.sim import SIM import yaml def main(args=None): """Main testing function""" action_dim = 2 # number of actions produced by the model max_action = 1 # maximum absolute value of output actions state_dim = 185 # number of input values in the neural network (vector length of state input) device = torch.device( "cuda" if torch.cuda.is_available() else "cpu" ) # using cuda if it is available, cpu otherwise epoch = 0 # epoch number max_steps = 300 # maximum number of steps in single episode model = RCPG( state_dim=state_dim, action_dim=action_dim, max_action=max_action, device=device, load_model=True, ) # instantiate a model sim = SIM(world_file="worlds/eval_world.yaml") # instantiate environment with open("robot_nav/eval_points.yaml") as file: points = yaml.safe_load(file) robot_poses = points["robot"]["poses"] robot_goals = points["robot"]["goals"] assert len(robot_poses) == len( robot_goals ), "Number of robot poses do not equal the robot goals" print("..............................................") print(f"Testing {len(robot_poses)} scenarios") total_reward = 0.0 total_steps = 0 col = 0 goals = 0 state_queue = deque(maxlen=5) for idx in range(len(robot_poses)): fill_state = True count = 0 latest_scan, distance, cos, sin, collision, goal, a, reward = sim.reset( robot_state=robot_poses[idx], robot_goal=robot_goals[idx], random_obstacles=False, ) done = False while not done and count < max_steps: state, terminal = model.prepare_state( latest_scan, distance, cos, sin, collision, goal, a ) if fill_state: state_queue.clear() for _ in range(5): state_queue.append(state) fill_state = False state_queue.append(state) action = model.get_action(np.array(state_queue), False) a_in = [(action[0] + 1) / 4, action[1]] latest_scan, distance, cos, sin, collision, goal, a, reward = sim.step( lin_velocity=a_in[0], ang_velocity=a_in[1] ) total_reward += reward total_steps += 1 count += 1 if collision: col += 1 if goal: goals += 1 done = collision or goal avg_step_reward = total_reward / total_steps avg_reward = total_reward / len(robot_poses) avg_col = col / len(robot_poses) avg_goal = goals / len(robot_poses) print(f"Total Reward: {total_reward}") print(f"Average Reward: {avg_reward}") print(f"Average Step Reward: {avg_step_reward}") print(f"Average Collision rate: {avg_col}") print(f"Average Goal rate: {avg_goal}") print("..............................................") model.writer.add_scalar("test/total_reward", total_reward, epoch) model.writer.add_scalar("test/avg_reward", avg_reward, epoch) model.writer.add_scalar("test/avg_step_reward", avg_step_reward, epoch) model.writer.add_scalar("test/avg_col", avg_col, epoch) model.writer.add_scalar("test/avg_goal", avg_goal, epoch) if __name__ == "__main__": main() ================================================ FILE: robot_nav/rnn_train.py ================================================ from robot_nav.models.RCPG.RCPG import RCPG from collections import deque import torch import numpy as np from robot_nav.SIM_ENV.sim import SIM from utils import get_buffer def main(args=None): """Main training function""" action_dim = 2 # number of actions produced by the model max_action = 1 # maximum absolute value of output actions state_dim = 185 # number of input values in the neural network (vector length of state input) device = torch.device( "cuda" if torch.cuda.is_available() else "cpu" ) # using cuda if it is available, cpu otherwise nr_eval_episodes = 10 # how many episodes to use to run evaluation max_epochs = 60 # max number of epochs epoch = 0 # starting epoch number episodes_per_epoch = 70 # how many episodes to run in single epoch episode = 0 # starting episode number train_every_n = 2 # train and update network parameters every n episodes training_iterations = 80 # how many batches to use for single training cycle batch_size = 64 # batch size for each training iteration max_steps = 300 # maximum number of steps in single episode steps = 0 # starting step number load_saved_buffer = False # whether to load experiences from assets/data.yml pretrain = False # whether to use the loaded experiences to pre-train the model (load_saved_buffer must be True) history_len = 10 pretraining_iterations = ( 10 # number of training iterations to run during pre-training ) save_every = 10 # save the model every n training cycles state_queue = deque(maxlen=history_len) model = RCPG( state_dim=state_dim, action_dim=action_dim, max_action=max_action, device=device, save_every=save_every, load_model=False, rnn="gru", ) # instantiate a model sim = SIM() # instantiate environment replay_buffer = get_buffer( model, sim, load_saved_buffer, pretrain, pretraining_iterations, training_iterations, batch_size, ) latest_scan, distance, cos, sin, collision, goal, a, reward = sim.step( lin_velocity=0.0, ang_velocity=0.0 ) # get the initial step state fill_state = True while epoch < max_epochs: # train until max_epochs is reached state, terminal = model.prepare_state( latest_scan, distance, cos, sin, collision, goal, a ) # get state a state representation from returned data from the environment if fill_state: state_queue.clear() for _ in range(history_len): state_queue.append(state) fill_state = False state_queue.append(state) action = model.get_action( np.array(state_queue), True ) # get an action from the model a_in = [ (action[0] + 1) / 4, action[1], ] # clip linear velocity to [0, 0.5] m/s range latest_scan, distance, cos, sin, collision, goal, a, reward = sim.step( lin_velocity=a_in[0], ang_velocity=a_in[1] ) # get data from the environment next_state, terminal = model.prepare_state( latest_scan, distance, cos, sin, collision, goal, a ) # get a next state representation replay_buffer.add( state, action, reward, terminal, next_state ) # add experience to the replay buffer if ( terminal or steps == max_steps ): # reset environment of terminal stat ereached, or max_steps were taken fill_state = True latest_scan, distance, cos, sin, collision, goal, a, reward = sim.reset() episode += 1 if episode % train_every_n == 0: model.train( replay_buffer=replay_buffer, iterations=training_iterations, batch_size=batch_size, ) # train the model and update its parameters steps = 0 else: steps += 1 if ( episode + 1 ) % episodes_per_epoch == 0: # if epoch is concluded, run evaluation episode = 0 epoch += 1 evaluate( model, epoch, sim, history_len, max_steps, eval_episodes=nr_eval_episodes, ) def evaluate(model, epoch, sim, history_len, max_steps, eval_episodes=10): print("..............................................") print(f"Epoch {epoch}. Evaluating scenarios") avg_reward = 0.0 col = 0 goals = 0 state_queue = deque(maxlen=history_len) for _ in range(eval_episodes): fill_state = True count = 0 latest_scan, distance, cos, sin, collision, goal, a, reward = sim.reset() done = False while not done and count < max_steps: state, terminal = model.prepare_state( latest_scan, distance, cos, sin, collision, goal, a ) if fill_state: state_queue.clear() for _ in range(history_len): state_queue.append(state) fill_state = False state_queue.append(state) action = model.get_action(np.array(state_queue), False) a_in = [(action[0] + 1) / 4, action[1]] latest_scan, distance, cos, sin, collision, goal, a, reward = sim.step( lin_velocity=a_in[0], ang_velocity=a_in[1] ) avg_reward += reward count += 1 if collision: col += 1 if goal: goals += 1 done = collision or goal avg_reward /= eval_episodes avg_col = col / eval_episodes avg_goal = goals / eval_episodes print(f"Average Reward: {avg_reward}") print(f"Average Collision rate: {avg_col}") print(f"Average Goal rate: {avg_goal}") print("..............................................") model.writer.add_scalar("eval/avg_reward", avg_reward, epoch) model.writer.add_scalar("eval/avg_col", avg_col, epoch) model.writer.add_scalar("eval/avg_goal", avg_goal, epoch) if __name__ == "__main__": main() ================================================ FILE: robot_nav/rvo_test_random.py ================================================ import statistics from tqdm import tqdm import torch import numpy as np import irsim import numpy as np import random import torch from robot_nav.SIM_ENV.sim_env import SIM_ENV class RVO_RANDOM_SIM(SIM_ENV): """ Simulation environment for randomized multi-agent robot navigation using IRSim. This class extends `SIM_ENV` and provides a wrapper for running a multi-robot simulation where robots navigate toward randomly assigned goals. It includes environment reset, step execution, and collision/goal state tracking. Attributes: env (object): IRSim simulation environment instance. robot_goal (np.ndarray): Current goal positions for all robots. num_robots (int): Number of robots in the environment. x_range (tuple): Range of the x-axis for the simulation world. y_range (tuple): Range of the y-axis for the simulation world. """ def __init__(self, world_file="multi_robot_world.yaml", disable_plotting=False): """ Initialize the randomized IRSim-based multi-robot environment. Args: world_file (str, optional): Path to the YAML configuration file defining the world layout. Defaults to "multi_robot_world.yaml". disable_plotting (bool, optional): If True, disables visual rendering and live plotting in IRSim. Defaults to False. """ display = False if disable_plotting else True self.env = irsim.make( world_file, disable_all_plot=disable_plotting, display=display ) robot_info = self.env.get_robot_info(0) self.robot_goal = robot_info.goal self.num_robots = len(self.env.robot_list) self.x_range = self.env._world.x_range self.y_range = self.env._world.y_range def step(self): """ Perform one simulation step for all robots and update goals dynamically. Each robot moves based on IRSim’s physics step, with collisions, goals, and positions collected. If a robot reaches its goal, a new random goal is automatically assigned. Returns: tuple: collisions (list of bool): Collision flags for each robot. goals (list of bool): Goal completion flags for each robot. positions (list of [float, float]): Current [x, y] positions for each robot. """ self.env.step() self.env.render() collisions = [] goals = [] positions = [] for i in range(self.num_robots): robot_state = self.env.robot_list[i].state position = [robot_state[0].item(), robot_state[1].item()] positions.append(position) goal = self.env.robot_list[i].arrive collision = self.env.robot_list[i].collision collisions.append(collision) goals.append(goal) if goal: self.env.robot_list[i].set_random_goal( obstacle_list=self.env.obstacle_list, init=True, range_limits=[ [self.x_range[0] + 1, self.y_range[0] + 1, -3.141592653589793], [self.x_range[1] - 1, self.y_range[1] - 1, 3.141592653589793], ], ) return collisions, goals, positions def reset( self, robot_state=None, robot_goal=None, random_obstacles=False, random_obstacle_ids=None, ): """ Reset the simulation environment, optionally randomizing robots and obstacles. Randomly reinitializes robot positions while avoiding collisions and can optionally re-randomize obstacle positions. Args: robot_state (list or None, optional): Custom initial state for robots as [x, y, theta, speed]. If None, random states are assigned. robot_goal (list or None, optional): Custom goal positions. If None, random goals are assigned within bounds. random_obstacles (bool, optional): Whether to randomize obstacle locations. Defaults to False. random_obstacle_ids (list or None, optional): List of obstacle IDs to randomize. If None, defaults to the first 7 obstacles after robots. Returns: tuple: collisions (list of bool): All False immediately after reset. goals (list of bool): All False immediately after reset. positions (list of [float, float]): Initial [x, y] positions for all robots. """ if robot_state is None: robot_state = [[random.uniform(3, 9)], [random.uniform(3, 9)], [0]] init_states = [] for robot in self.env.robot_list: conflict = True while conflict: conflict = False robot_state = [ [random.uniform(3, 9)], [random.uniform(3, 9)], [random.uniform(-3.14, 3.14)], ] pos = [robot_state[0][0], robot_state[1][0]] for loc in init_states: vector = [ pos[0] - loc[0], pos[1] - loc[1], ] if np.linalg.norm(vector) < 0.6: conflict = True init_states.append(pos) robot.set_state( state=np.array(robot_state), init=True, ) if random_obstacles: if random_obstacle_ids is None: random_obstacle_ids = [i + self.num_robots for i in range(7)] self.env.random_obstacle_position( range_low=[self.x_range[0], self.y_range[0], -3.14], range_high=[self.x_range[1], self.y_range[1], 3.14], ids=random_obstacle_ids, non_overlapping=True, ) for robot in self.env.robot_list: if robot_goal is None: robot.set_random_goal( obstacle_list=self.env.obstacle_list, init=True, range_limits=[ [self.x_range[0] + 1, self.y_range[0] + 1, -3.141592653589793], [self.x_range[1] - 1, self.y_range[1] - 1, 3.141592653589793], ], ) else: self.env.robot.set_goal(np.array(robot_goal), init=True) self.env.reset() self.robot_goal = self.env.robot.goal _, _, positions = self.step() return [False] * self.num_robots, [False] * self.num_robots, positions def get_reward(self): """ Placeholder for computing reward functions for the simulation. Intended for integration with learning-based algorithms (e.g. MARL), calculating per-agent or global rewards based on goal progress, collisions, or path efficiency. Returns: None """ pass def outside_of_bounds(poses): """ Determine whether any robot is outside the simulation’s defined world boundary. The environment is assumed to be centered at (6, 6) with a 21×21 area. Args: poses (list of [float, float, float]): Robot poses in [x, y, theta] format. Returns: bool: True if any robot is outside the world boundary; False otherwise. """ outside = False for pose in poses: norm_x = pose[0] - 6 norm_y = pose[1] - 6 if abs(norm_x) > 10.5 or abs(norm_y) > 10.5: outside = True break return outside def main(args=None): """ Run multiple randomized test scenarios in the RVO_RANDOM_SIM environment. Executes a series of randomized navigation tests, stepping through the environment while tracking the number of goals reached, collisions, and time steps per episode. Prints out aggregated performance metrics across all episodes. Args: args (Namespace or None, optional): Optional runtime arguments (unused). Prints: Summary statistics including: - Average collisions per episode (and standard deviation) - Average goals per episode (and standard deviation) """ episode = 0 max_steps = 300 # maximum number of steps in single episode steps = 0 # starting step number test_scenarios = 1000 # ---- Instantiate simulation environment and model ---- sim = RVO_RANDOM_SIM( world_file="worlds/multi_robot_world.yaml", disable_plotting=True ) # instantiate environment running_goals = 0 running_collisions = 0 running_timesteps = 0 goals_per_ep = [] col_per_ep = [] pbar = tqdm(total=test_scenarios) # ---- Main training loop ---- while episode < test_scenarios: collision, goal, poses = sim.step() # get data from the environment running_goals += sum(goal) running_collisions += sum(collision) running_timesteps += 1 outside = outside_of_bounds(poses) if ( sum(collision) > 0.5 or steps == max_steps or outside ): # reset environment of terminal state reached, or max_steps were taken sim.reset() goals_per_ep.append(running_goals) running_goals = 0 col_per_ep.append(running_collisions) running_collisions = 0 steps = 0 episode += 1 pbar.update(1) else: steps += 1 goals_per_ep = np.array(goals_per_ep, dtype=np.float32) col_per_ep = np.array(col_per_ep, dtype=np.float32) avg_ep_col = statistics.mean(col_per_ep) avg_ep_col_std = statistics.stdev(col_per_ep) avg_ep_goals = statistics.mean(goals_per_ep) avg_ep_goals_std = statistics.stdev(goals_per_ep) print(f"avg_ep_col: {avg_ep_col}") print(f"avg_ep_col_std: {avg_ep_col_std}") print(f"avg_ep_goals: {avg_ep_goals}") print(f"avg_ep_goals_std: {avg_ep_goals_std}") print("..............................................") if __name__ == "__main__": main() ================================================ FILE: robot_nav/rvo_test_single.py ================================================ import statistics from tqdm import tqdm import irsim import numpy as np import random import torch from robot_nav.SIM_ENV.sim_env import SIM_ENV class RVO_SINGLE_SIM(SIM_ENV): """ Simulation environment for single-scenario multi-robot navigation using IRSim. This class extends `SIM_ENV` and wraps the IRSim-based environment to simulate multiple robots navigating toward goals while avoiding collisions. It provides reset and step functionality with boundary and collision checks. Attributes: env (object): IRSim simulation environment instance. robot_goal (np.ndarray): Current goal position(s) for all robots. num_robots (int): Total number of robots in the environment. x_range (tuple): World horizontal range (min_x, max_x). y_range (tuple): World vertical range (min_y, max_y). """ def __init__(self, world_file="multi_robot_world.yaml", disable_plotting=False): """ Initialize the IRSim-based multi-robot simulation environment. Args: world_file (str, optional): Path to the YAML configuration for the world. Defaults to "multi_robot_world.yaml". disable_plotting (bool, optional): If True, disables rendering and live plotting. Defaults to False. """ display = False if disable_plotting else True self.env = irsim.make( world_file, disable_all_plot=disable_plotting, display=display ) robot_info = self.env.get_robot_info(0) self.robot_goal = robot_info.goal self.num_robots = len(self.env.robot_list) self.x_range = self.env._world.x_range self.y_range = self.env._world.y_range def step(self): """ Perform one simulation step for all robots and retrieve updated environment state. Executes the IRSim environment's step and render functions, and gathers each robot’s position, goal status, and collision flag. Returns: tuple: collisions (list of bool): Collision flags for each robot. goals (list of bool): Goal reached flags for each robot. positions (list of [float, float]): Current [x, y] positions of robots. """ self.env.step() self.env.render() collisions = [] goals = [] positions = [] for i in range(self.num_robots): robot_state = self.env.robot_list[i].state position = [robot_state[0].item(), robot_state[1].item()] positions.append(position) goal = self.env.robot_list[i].arrive collision = self.env.robot_list[i].collision collisions.append(collision) goals.append(goal) return collisions, goals, positions def reset( self, robot_state=None, robot_goal=None, random_obstacles=False, random_obstacle_ids=None, ): """ Reset the simulation environment and optionally randomize robot or obstacle positions. Args: robot_state (list or None, optional): Custom initial states for robots as [x, y, theta, speed]. If None, resets to defaults. robot_goal (list or None, optional): Goal positions for robots. If None, goals are retained from the previous configuration. random_obstacles (bool, optional): Whether to randomize obstacle positions. Defaults to False. random_obstacle_ids (list or None, optional): List of obstacle IDs to randomize. Defaults to None. Returns: tuple: collisions (list of bool): All False after reset. goals (list of bool): All False after reset. positions (list of [float, float]): Initial [x, y] positions for each robot. """ self.env.reset() self.robot_goal = self.env.robot.goal _, _, positions = self.step() return [False] * self.num_robots, [False] * self.num_robots, positions def get_reward(self): """ Compute reward signals for each robot. This method is a placeholder for reward computation logic based on task progress, collisions, or goal attainment. Returns: None """ pass def outside_of_bounds(poses): """ Check whether any robot is outside the defined simulation world boundaries. The world is assumed to be centered around (6, 6) with a width and height of 21 units. Args: poses (list of [float, float, float]): Robot poses [x, y, theta] for all robots. Returns: bool: True if any robot is outside the bounds (|x-6|>10.5 or |y-6|>10.5), else False. """ outside = False for pose in poses: norm_x = pose[0] - 6 norm_y = pose[1] - 6 if abs(norm_x) > 10.5 or abs(norm_y) > 10.5: outside = True break return outside def main(args=None): """ Run the evaluation loop for multiple test scenarios in the RVO_SINGLE_SIM environment. Executes a number of test episodes, stepping through the simulation, tracking goals, collisions, and timeouts, and summarizing performance statistics. Args: args (Namespace or None, optional): Optional runtime arguments (unused). Prints: - Average collisions per episode and standard deviation. - Average goals per episode and standard deviation. - Average timesteps per episode and standard deviation. - Success rate and timeout count. """ episode = 0 max_steps = 300 # maximum number of steps in single episode steps = 0 # starting step number test_scenarios = 100 # ---- Instantiate simulation environment and model ---- sim = RVO_SINGLE_SIM( world_file="worlds/circle_world.yaml", disable_plotting=True ) # instantiate environment running_goals = 0 running_collisions = 0 running_timesteps = 0 ran_out_of_time = 0 goals_per_ep = [] col_per_ep = [] timesteps_per_ep = [] pbar = tqdm(total=test_scenarios) # ---- Main training loop ---- while episode < test_scenarios: collision, goal, poses = sim.step() # get data from the environment running_goals += sum(goal) running_collisions += sum(collision) / 2 running_timesteps += 1 outside = outside_of_bounds(poses) if ( sum(collision) > 0.5 or steps == max_steps or outside or int(sum(goal)) == len(goal) ): # reset environment of terminal state reached, or max_steps were taken sim.reset() goals_per_ep.append(running_goals) running_goals = 0 col_per_ep.append(running_collisions) running_collisions = 0 timesteps_per_ep.append(running_timesteps) running_timesteps = 0 if steps == max_steps: ran_out_of_time += 1 steps = 0 episode += 1 pbar.update(1) else: steps += 1 cols = sum(col_per_ep) goals_per_ep = np.array(goals_per_ep, dtype=np.float32) col_per_ep = np.array(col_per_ep, dtype=np.float32) t_per_ep = np.array(timesteps_per_ep, dtype=np.float32) avg_ep_col = statistics.mean(col_per_ep) avg_ep_col_std = statistics.stdev(col_per_ep) avg_ep_goals = statistics.mean(goals_per_ep) avg_ep_goals_std = statistics.stdev(goals_per_ep) avg_ep_t = statistics.mean(t_per_ep) avg_ep_t_std = statistics.stdev(t_per_ep) print(f"avg_ep_col: {avg_ep_col}") print(f"avg_ep_col_std: {avg_ep_col_std}") print(f"success rate: {test_scenarios - cols}") print(f"avg_ep_goals: {avg_ep_goals}") print(f"avg_ep_goals_std: {avg_ep_goals_std}") print(f"avg_ep_t: {avg_ep_t}") print(f"avg_ep_t_std: {avg_ep_t_std}") print(f"ran out of time: {ran_out_of_time}") print("..............................................") if __name__ == "__main__": main() ================================================ FILE: robot_nav/utils.py ================================================ from typing import List from tqdm import tqdm import yaml import torch from robot_nav.models.RCPG.RCPG import RCPG from robot_nav.replay_buffer import ReplayBuffer, RolloutReplayBuffer from robot_nav.models.PPO.PPO import PPO class Pretraining: """ Handles loading of offline experience data and pretraining of a reinforcement learning model. Attributes: file_names (List[str]): List of YAML files containing pre-recorded environment samples. model (object): The model with `prepare_state` and `train` methods. replay_buffer (object): The buffer used to store experiences for training. reward_function (callable): Function to compute the reward from the environment state. """ def __init__( self, file_names: List[str], model: object, replay_buffer: object, reward_function, ): self.file_names = file_names self.model = model self.replay_buffer = replay_buffer self.reward_function = reward_function def load_buffer(self): """ Load samples from the specified files and populate the replay buffer. Returns: (object): The populated replay buffer. """ for file_name in self.file_names: print("Loading file: ", file_name) with open(file_name, "r") as file: samples = yaml.full_load(file) for i in tqdm(range(1, len(samples) - 1)): sample = samples[i] latest_scan = sample["latest_scan"] distance = sample["distance"] cos = sample["cos"] sin = sample["sin"] collision = sample["collision"] goal = sample["goal"] action = sample["action"] state, terminal = self.model.prepare_state( latest_scan, distance, cos, sin, collision, goal, action ) if terminal: continue next_sample = samples[i + 1] next_latest_scan = next_sample["latest_scan"] next_distance = next_sample["distance"] next_cos = next_sample["cos"] next_sin = next_sample["sin"] next_collision = next_sample["collision"] next_goal = next_sample["goal"] next_action = next_sample["action"] next_state, next_terminal = self.model.prepare_state( next_latest_scan, next_distance, next_cos, next_sin, next_collision, next_goal, next_action, ) reward = self.reward_function( next_goal, next_collision, action, next_latest_scan ) self.replay_buffer.add( state, action, reward, next_terminal, next_state ) return self.replay_buffer def train( self, pretraining_iterations, replay_buffer, iterations, batch_size, ): """ Run pretraining on the model using the replay buffer. Args: pretraining_iterations (int): Number of outer loop iterations for pretraining. replay_buffer (object): Buffer to sample training batches from. iterations (int): Number of training steps per pretraining iteration. batch_size (int): Batch size used during training. """ print("Running Pretraining") for _ in tqdm(range(pretraining_iterations)): self.model.train( replay_buffer=replay_buffer, iterations=iterations, batch_size=batch_size, ) print("Model Pretrained") def get_buffer( model, sim, load_saved_buffer, pretrain, pretraining_iterations, training_iterations, batch_size, buffer_size=50000, random_seed=666, file_names=["robot_nav/assets/data.yml"], history_len=10, ): """ Get or construct the replay buffer depending on model type and training configuration. Args: model (object): The RL model, can be PPO, RCPG, or other. sim (object): Simulation environment with a `get_reward` function. load_saved_buffer (bool): Whether to load experiences from file. pretrain (bool): Whether to run pretraining using the buffer. pretraining_iterations (int): Number of outer loop iterations for pretraining. training_iterations (int): Number of iterations in each training loop. batch_size (int): Size of the training batch. buffer_size (int, optional): Maximum size of the buffer. Defaults to 50000. random_seed (int, optional): Seed for reproducibility. Defaults to 666. file_names (List[str], optional): List of YAML data file paths. Defaults to ["robot_nav/assets/data.yml"]. history_len (int, optional): Used for RCPG buffer configuration. Defaults to 10. Returns: (object): The initialized and optionally pre-populated replay buffer. """ if isinstance(model, PPO): return model.buffer if isinstance(model, RCPG): replay_buffer = RolloutReplayBuffer( buffer_size=buffer_size, random_seed=random_seed, history_len=history_len ) else: replay_buffer = ReplayBuffer(buffer_size=buffer_size, random_seed=random_seed) if pretrain: assert ( load_saved_buffer ), "To pre-train model, load_saved_buffer must be set to True" if load_saved_buffer: pretraining = Pretraining( file_names=file_names, model=model, replay_buffer=replay_buffer, reward_function=sim.get_reward, ) # instantiate pre-trainind replay_buffer = ( pretraining.load_buffer() ) # fill buffer with experiences from the data.yml file if pretrain: pretraining.train( pretraining_iterations=pretraining_iterations, replay_buffer=replay_buffer, iterations=training_iterations, batch_size=batch_size, ) # run pre-training return replay_buffer def get_max_bound( next_state, discount, max_ang_vel, max_lin_vel, time_step, distance_norm, goal_reward, reward, done, device, ): """ Estimate the maximum possible return (upper bound) from the next state onward. This is used in constrained RL or safe policy optimization where a conservative estimate of return is useful for policy updates. Args: next_state (torch.Tensor): Tensor of next state observations. discount (float): Discount factor for future rewards. max_ang_vel (float): Maximum angular velocity of the agent. max_lin_vel (float): Maximum linear velocity of the agent. time_step (float): Duration of one time step. distance_norm (float): Normalization factor for distance. goal_reward (float): Reward received upon reaching the goal. reward (torch.Tensor): Immediate reward from the environment. done (torch.Tensor): Binary tensor indicating episode termination. device (torch.device): PyTorch device for computation. Returns: (torch.Tensor): Maximum return bound for each sample in the batch. """ next_state = next_state.clone() # Prevents in-place modifications reward = reward.clone() # Ensures original reward is unchanged done = done.clone() cos = next_state[:, -4] sin = next_state[:, -3] theta = torch.atan2(sin, cos) # Compute turning steps turn_steps = theta.abs() / (max_ang_vel * time_step) full_turn_steps = torch.floor(turn_steps) turn_rew = -max_ang_vel * discount**full_turn_steps turn_rew[full_turn_steps == 0] = 0 # Handle zero case final_turn_rew = -(discount ** (full_turn_steps + 1)) * ( turn_steps - full_turn_steps ) full_turn_rew = turn_rew + final_turn_rew # Compute distance-based steps full_turn_steps += 1 # Account for the final turn step distances = (next_state[:, -5] * distance_norm) / (max_lin_vel * time_step) final_steps = torch.ceil(distances) + full_turn_steps inter_steps = torch.trunc(distances) + full_turn_steps final_rew = goal_reward * discount**final_steps # Compute intermediate rewards using a sum of discounted steps max_inter_steps = inter_steps.max().int().item() discount_exponents = discount ** torch.arange(1, max_inter_steps + 1, device=device) inter_rew = torch.stack( [ (max_lin_vel * discount_exponents[int(start) + 1 : int(steps)]).sum() for start, steps in zip(full_turn_steps, inter_steps) ] ) # Compute final max bound max_bound = reward + (1 - done) * (full_turn_rew + final_rew + inter_rew).view( -1, 1 ) return max_bound ================================================ FILE: robot_nav/worlds/circle_world.yaml ================================================ world: height: 12 # the height of the world width: 12 # the height of the world step_time: 0.3 # Calculate each step sample_time: 0.3 # For render and data extraction collision_mode: 'reactive' robot: - number: 5 kinematics: {name: 'diff'} distribution: {name: 'circle', radius: 5, center: [6, 6]} shape: {name: 'circle', radius: 0.2} # behavior: {name: 'rvo', mode: 'rvo', wander: False, neighbor_threshold: 4.0} # use behavior for ROV method vel_min: [ 0, -1.0 ] vel_max: [ 0.5, 1.0 ] state: [2, 2, 0, 0] goal: [9, 9, 0] color: ['royalblue', 'red', 'green', 'orange', 'purple', 'yellow', 'cyan', 'magenta', 'lime', 'pink', 'brown', 'black', 'bisque', 'slategrey', 'lavender', 'hotpink', 'navy', 'gold', 'olive', 'peru'] arrive_mode: position goal_threshold: 0.3 plot: show_trajectory: False ================================================ FILE: robot_nav/worlds/cross_world.yaml ================================================ world: height: 12 # the height of the world width: 12 # the height of the world step_time: 0.3 # Calculate each step sample_time: 0.3 # For render and data extraction collision_mode: 'reactive' robot: - number: 5 kinematics: {name: 'diff'} distribution: {name: 'manual'} shape: {name: 'circle', radius: 0.2} # behavior: {name: 'rvo', mode: 'rvo', wander: False, neighbor_threshold: 4.0} # use behavior for ROV method vel_min: [ 0, -1.0 ] vel_max: [ 0.5, 1.0 ] state: [[1, 3, 1.57], [9, 8, -1.57], [3, 3, 1.57], [7, 8, -1.57], [5, 3, 1.57], [5, 8, -1.57], [7, 3, 1.57], [3, 8, -1.57], [9, 3, 1.57], [1, 8, -1.57], [6, 3, 1.57], [6, 7, -1.57], [7, 1, 1.57], [7, 10, -1.57], [8, 3, 1.57], [8, 7, -1.57], [9, 1, 1.57], [9, 10, -1.57], [10, 3, 1.57], [10, 7, -1.57]] goal: [[9, 8, 0], [1, 3, 0], [7, 8, 0], [3, 3, 0], [5, 8, 0], [5, 3, 0], [3, 8, 0], [7, 3, 0], [1, 8, 0], [9, 3, 0], [9, 1, 0], [6, 3, 0], [7, 10, 0], [7, 1, 0], [8, 7, 0], [8, 3, 0], [9, 10, 0], [9, 1, 0], [10, 7, 0], [10, 3, 0]] color: ['royalblue', 'red', 'green', 'orange', 'purple', 'yellow', 'cyan', 'magenta', 'lime', 'pink', 'brown'] arrive_mode: position goal_threshold: 0.3 plot: show_trajectory: False ================================================ FILE: robot_nav/worlds/eval_world.yaml ================================================ world: height: 10 # the height of the world width: 10 # the height of the world step_time: 0.3 # Hz calculate each step sample_time: 0.3 # Hz for render and data extraction collision_mode: 'reactive' robot: - kinematics: {name: 'diff'} shape: {name: 'circle', radius: 0.2} vel_min: [ 0, -1.0 ] vel_max: [ 1.0, 1.0 ] state: [3, 4, 0, 0] goal: [9, 9, 0] arrive_mode: position goal_threshold: 0.3 sensors: - type: 'lidar2d' range_min: 0 range_max: 7 angle_range: 3.14 number: 180 noise: True std: 0.08 angle_std: 0.1 offset: [ 0, 0, 0 ] alpha: 0.3 plot: show_trajectory: True obstacle: - shape: { name: 'circle', radius: 1.0 } # radius state: [ 5, 5, 0 ] - shape: { name: 'circle', radius: 0.5 } # radius state: [ 7, 8, 0 ] - shape: { name: 'circle', radius: 1.4 } # radius state: [ 3, 1, 0 ] - shape: {name: 'rectangle', length: 1.0, width: 1.2} # length, width state: [8, 5, 1] - shape: { name: 'rectangle', length: 0.5, width: 2.1 } # length, width state: [ 1, 8, 1.3 ] - shape: { name: 'rectangle', length: 1.5, width: 0.7 } # length, width state: [ 6, 2, 0.5 ] - shape: { name: 'linestring', vertices: [ [ 0, 0 ], [ 10, 0 ], [ 10, 10 ], [ 0, 10 ],[ 0, 0 ] ] } # vertices kinematics: {name: 'static'} state: [ 0, 0, 0 ] ================================================ FILE: robot_nav/worlds/multi_robot_world.yaml ================================================ world: height: 12 # the height of the world width: 12 # the height of the world step_time: 0.3 # Calculate each step sample_time: 0.3 # For render and data extraction collision_mode: 'reactive' robot: - number: 5 kinematics: {name: 'diff'} distribution: {name: 'circle', radius: 4, center: [6, 6]} shape: {name: 'circle', radius: 0.2} vel_min: [ 0, -1.0 ] vel_max: [ 0.5, 1.0 ] state: [2, 2, 0, 0] goal: [9, 9, 0] color: ['royalblue', 'red', 'green', 'orange', 'purple', 'yellow', 'cyan', 'magenta', 'lime', 'pink', 'brown', 'black', 'bisque', 'slategrey', 'lavender', 'hotpink', 'navy', 'gold', 'olive', 'peru'] arrive_mode: position goal_threshold: 0.3 plot: show_trajectory: False ================================================ FILE: robot_nav/worlds/robot_world.yaml ================================================ world: height: 10 # the height of the world width: 10 # the height of the world step_time: 0.3 # 10Hz calculate each step sample_time: 0.3 # 10 Hz for render and data extraction collision_mode: 'reactive' robot: - kinematics: {name: 'diff'} shape: {name: 'circle', radius: 0.2} vel_min: [ 0, -1.0 ] vel_max: [ 1.0, 1.0 ] state: [2, 2, 0, 0] goal: [9, 9, 0] arrive_mode: position goal_threshold: 0.3 sensors: - type: 'lidar2d' range_min: 0 range_max: 7 angle_range: 3.14 number: 180 noise: True std: 0.08 angle_std: 0.1 offset: [ 0, 0, 0 ] alpha: 0.3 plot: show_trajectory: True obstacle: - number: 4 kinematics: {name: 'omni'} distribution: {name: 'random', range_low: [0, 0, -3.14], range_high: [10, 10, 3.14]} behavior: {name: 'rvo', wander: True, range_low: [0, 0, -3.14], range_high: [10, 10, 3.14], vxmax: 0.2, vymax: 0.2, factor: 1.0} vel_max: [0.2, 0.2] vel_min: [-0.2, -0.2] shape: - {name: 'circle', radius: 1.0, random_shape: True} - {name: 'polygon', random_shape: true, avg_radius_range: [0.5, 1.0], irregularity_range: [0, 0.4], spikeyness_range: [0, 0.4], num_vertices_range: [4, 6]} - shape: {name: 'circle', radius: 0.8} # length, width state: [ 5, 2, 1 ] kinematics: { name: 'static' } - shape: { name: 'rectangle', length: 1.0, width: 1.2 } # length, width state: [ 8, 5, 1 ] kinematics: {name: 'static'} - shape: { name: 'rectangle', length: 0.5, width: 2.1 } # length, width state: [ 1, 8, 1.3 ] kinematics: {name: 'static'} - shape: { name: 'linestring', vertices: [ [ 0, 0 ], [ 10, 0 ], [ 10, 10 ], [ 0, 10 ],[ 0, 0 ] ] } # vertices kinematics: {name: 'static'} state: [ 0, 0, 0 ] ================================================ FILE: tests/__init__.py ================================================ ================================================ FILE: tests/test_data.yml ================================================ 1: action: - 0.0 - 0.0 collision: false cos: 0.7071067811865476 distance: 9.899494936611665 goal: false latest_scan: - 1.8665991590136355 - 1.9946768996645663 - 1.9665565739935549 - 1.9788152897656712 - 1.8973958875046795 - 1.9157427843906638 - 2.0529971058434335 - 2.1046455362962297 - 2.041846121445663 - 2.0293344301200666 - 2.00356377871485 - 2.0532115686525954 - 1.9313506399651403 - 1.9365900577565431 - 2.0091760652957387 - 1.9885402993956873 - 2.141310656736781 - 2.0958840773768412 - 2.212509071184679 - 2.021341688404359 - 2.0606102243484727 - 2.236829156807564 - 2.0698016359222984 - 2.185472738047831 - 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4.755028961800581 - 4.920711163647003 - 4.689181624795308 - 4.765073167289759 - 4.559605896169577 - 4.729950901233965 - 4.660697429427393 - 4.5998010727234115 - 4.743477846779472 - 4.648961083837353 - 4.56699327108042 - 4.634580747834381 - 4.8227872093656705 - 4.64247859062304 - 4.70540500439634 - 4.622982748067758 - 4.636431692014926 - 4.696630214993021 - 4.742681199720893 - 4.808072767156871 - 4.191335808796117 sin: -0.3955797492530232 ================================================ FILE: tests/test_marl_world.yaml ================================================ world: height: 12 # the height of the world width: 12 # the height of the world step_time: 1.0 sample_time: 1.0 collision_mode: 'reactive' robot: - number: 3 kinematics: {name: 'diff'} distribution: {name: 'manual'} shape: {name: 'circle', radius: 0.2} vel_min: [ 0, -1.0 ] vel_max: [ 1.0, 1.0 ] state: [[3, 10, 0], [3, 6, 0], [3, 2, 0]] goal: [[9, 9, 0], [8, 8, 0], [7, 7, 0]] color: ['royalblue', 'red', 'green', 'orange', 'purple', 'yellow', 'cyan', 'magenta', 'lime', 'pink', 'brown'] arrive_mode: position goal_threshold: 0.3 plot: show_trajectory: False ================================================ FILE: tests/test_model.py ================================================ from pathlib import Path import torch from robot_nav.SIM_ENV.marl_sim import MARL_SIM from robot_nav.models.RCPG.RCPG import RCPG from robot_nav.models.TD3.TD3 import TD3 from robot_nav.models.CNNTD3.CNNTD3 import CNNTD3 from robot_nav.models.SAC.SAC import SAC from robot_nav.models.DDPG.DDPG import DDPG from robot_nav.utils import get_buffer from robot_nav.SIM_ENV.sim import SIM from robot_nav.models.MARL.marlTD3.marlTD3 import TD3 as marlTD3 import pytest PROJECT_ROOT = Path(__file__).resolve().parents[1] @pytest.mark.parametrize( "model, state_dim", [ (RCPG, 185), (CNNTD3, 185), (TD3, 10), (SAC, 10), (DDPG, 10), ], ) def test_models(model, state_dim): test_model = model( state_dim=state_dim, action_dim=2, max_action=1, device="cpu", save_every=0, load_model=False, ) # instantiate a model sim = SIM prefilled_buffer = get_buffer( model=test_model, sim=sim, load_saved_buffer=True, pretrain=False, pretraining_iterations=0, training_iterations=0, batch_size=0, buffer_size=100, file_names=[PROJECT_ROOT.joinpath("tests/test_data.yml")], ) test_model.train( replay_buffer=prefilled_buffer, iterations=2, batch_size=8, ) @pytest.mark.parametrize( "model, state_dim", [ (CNNTD3, 185), (TD3, 10), (DDPG, 10), ], ) def test_max_bound_models(model, state_dim): test_model = model( state_dim=state_dim, action_dim=2, max_action=1, device="cpu", save_every=0, load_model=False, use_max_bound=True, ) # instantiate a model sim = SIM prefilled_buffer = get_buffer( model=test_model, sim=sim, load_saved_buffer=True, pretrain=False, pretraining_iterations=0, training_iterations=0, batch_size=0, buffer_size=100, file_names=[PROJECT_ROOT.joinpath("tests/test_data.yml")], ) test_model.train( replay_buffer=prefilled_buffer, iterations=2, batch_size=8, ) def test_marl_models(): sim = MARL_SIM("/tests/test_marl_world.yaml", disable_plotting=True) test_model = marlTD3( state_dim=11, action_dim=2, max_action=1, num_robots=sim.num_robots, device="cpu", save_every=0, load_model=False, ) # instantiate a model replay_buffer = get_buffer( model=test_model, sim=sim, load_saved_buffer=False, pretrain=False, pretraining_iterations=0, training_iterations=0, batch_size=0, buffer_size=100, ) for _ in range(10): connections = torch.tensor( [[0.0 for _ in range(sim.num_robots - 1)] for _ in range(3)] ) ( poses, distance, cos, sin, collision, goal, a, reward, positions, goal_positions, ) = sim.step([[0, 0] for _ in range(sim.num_robots)], connections) state, terminal = test_model.prepare_state( poses, distance, cos, sin, collision, a, goal_positions ) replay_buffer.add( state, [[0, 0] for _ in range(sim.num_robots)], reward, terminal, state ) test_model.train( replay_buffer=replay_buffer, iterations=2, batch_size=8, ) ================================================ FILE: tests/test_sim.py ================================================ import os import pytest import torch from robot_nav.SIM_ENV.marl_sim import MARL_SIM from robot_nav.SIM_ENV.sim import SIM import numpy as np skip_on_ci = pytest.mark.skipif( os.getenv("CI") == "true", reason="Skipped on CI (GitHub Actions)" ) @skip_on_ci def test_sim(): sim = SIM("/tests/test_world.yaml", disable_plotting=True) robot_state = sim.env.get_robot_state() state = sim.step(1, 0) next_robot_state = sim.env.get_robot_state() assert np.isclose(robot_state[0], next_robot_state[0] - 1) assert np.isclose(robot_state[1], robot_state[1]) assert len(state[0]) == 180 assert len(sim.env.obstacle_list) == 7 sim.reset(random_obstacle_ids=[i + 1 for i in range(6)]) new_robot_state = sim.env.get_robot_state() assert np.not_equal(robot_state[0], new_robot_state[0]) assert np.not_equal(robot_state[1], new_robot_state[1]) def test_marl_sim(): sim = MARL_SIM("/tests/test_marl_world.yaml", disable_plotting=True) robot_state = [sim.env.robot_list[i].state[:2] for i in range(3)] connections = torch.tensor( [[0.0 for _ in range(sim.num_robots - 1)] for _ in range(3)] ) _ = sim.step([[1, 0], [1, 0], [1, 0]], connections) next_robot_state = [sim.env.robot_list[i].state[:2] for i in range(3)] for j in range(3): assert np.isclose(robot_state[j][0], next_robot_state[j][0] - 1) assert np.isclose(robot_state[j][1], robot_state[j][1]) assert len(sim.env.obstacle_list) == 0 sim.reset() new_robot_state = [sim.env.robot_list[i].state[:2] for i in range(3)] for j in range(3): assert np.not_equal(robot_state[j][0], new_robot_state[j][0]) assert np.not_equal(robot_state[j][1], new_robot_state[j][1]) @skip_on_ci def test_sincos(): sim = SIM("/tests/test_world.yaml") cos, sin = sim.cossin([1, 0], [0, 1]) assert np.isclose(cos, 0) assert np.isclose(sin, 1) ================================================ FILE: tests/test_utils.py ================================================ from pathlib import Path from robot_nav.models.SAC.SAC import SAC from robot_nav.models.PPO.PPO import PPO, RolloutBuffer from robot_nav.models.RCPG.RCPG import RCPG from robot_nav.utils import get_buffer, RolloutReplayBuffer, ReplayBuffer from robot_nav.SIM_ENV.sim import SIM PROJECT_ROOT = Path(__file__).resolve().parents[1] def test_buffer(): model = SAC( state_dim=10, action_dim=2, max_action=1, device="cpu", save_every=0, load_model=False, ) # instantiate a model sim = SIM buffer = get_buffer( model=model, sim=sim, load_saved_buffer=False, pretrain=False, pretraining_iterations=0, training_iterations=0, batch_size=0, buffer_size=100, ) assert buffer.count == 0 assert isinstance(buffer, ReplayBuffer) prefilled_buffer = get_buffer( model=model, sim=sim, load_saved_buffer=True, pretrain=False, pretraining_iterations=0, training_iterations=0, batch_size=0, buffer_size=100, file_names=[PROJECT_ROOT.joinpath("tests/test_data.yml")], ) assert prefilled_buffer.count == 100 def test_rollout_buffer(): model = RCPG( state_dim=185, action_dim=2, max_action=1, device="cpu", save_every=0, load_model=False, ) # instantiate a model sim = SIM buffer = get_buffer( model=model, sim=sim, load_saved_buffer=False, pretrain=False, pretraining_iterations=0, training_iterations=0, batch_size=0, buffer_size=100, ) assert buffer.count == 0 assert isinstance(buffer, RolloutReplayBuffer) prefilled_buffer = get_buffer( model=model, sim=sim, load_saved_buffer=True, pretrain=False, pretraining_iterations=0, training_iterations=0, batch_size=0, buffer_size=100, file_names=[PROJECT_ROOT.joinpath("tests/test_data.yml")], ) assert prefilled_buffer.count == 6 def test_ppo_buffer(): model = PPO( state_dim=10, action_dim=10, max_action=1, device="cpu", save_every=0, load_model=False, ) sim = SIM buffer = get_buffer( model=model, sim=sim, load_saved_buffer=False, pretrain=False, pretraining_iterations=0, training_iterations=0, batch_size=0, buffer_size=100, ) assert isinstance(buffer, RolloutBuffer) ================================================ FILE: tests/test_world.yaml ================================================ world: height: 10 # the height of the world width: 10 # the height of the world step_time: 1.0 sample_time: 1.0 collision_mode: 'reactive' robot: - kinematics: {name: 'diff'} shape: {name: 'circle', radius: 0.2} vel_min: [ 0, -1.0 ] vel_max: [ 1.0, 1.0 ] state: [3, 4, 0, 0] goal: [9, 9, 0] arrive_mode: position goal_threshold: 0.3 sensors: - type: 'lidar2d' range_min: 0 range_max: 7 angle_range: 3.14 number: 180 noise: True std: 0.08 angle_std: 0.1 offset: [ 0, 0, 0 ] alpha: 0.3 plot: show_trajectory: True obstacle: - shape: { name: 'circle', radius: 1.0 } # radius state: [ 5, 5, 0 ] - shape: { name: 'circle', radius: 0.5 } # radius state: [ 7, 8, 0 ] - shape: { name: 'circle', radius: 1.4 } # radius state: [ 3, 1, 0 ] - shape: {name: 'rectangle', length: 1.0, width: 1.2} # length, width state: [8, 5, 1] - shape: { name: 'rectangle', length: 0.5, width: 2.1 } # length, width state: [ 1, 8, 1.3 ] - shape: { name: 'rectangle', length: 1.5, width: 0.7 } # length, width state: [ 6, 2, 0.5 ] - shape: { name: 'linestring', vertices: [ [ 0, 0 ], [ 10, 0 ], [ 10, 10 ], [ 0, 10 ],[ 0, 0 ] ] } # vertices kinematics: {name: 'static'} state: [ 0, 0, 0 ]