[ { "path": "LICENSE", "content": "The MIT License (MIT)\n\nCopyright (c) 2019 Liulong Ma\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n" }, { "path": "README.md", "content": "# navbot\n It's a collection for mapless robot navigation using RGB image as visual input. It contains the test \n environment and motion planners, aiming at realizing all the three levels of mapless navigation:\n 1. memorizing efficiently; \n 2. from memorizing to reasoning; \n 3. more powerful reasoning\n The experiment data is in ./materials/record folder. \n\n\"\"
\n\n## Environment \nI built the environment as benchmark for testing the algorithms.
\n\n\"\" \nIt has the following properties: \n\n* Diverse complexity. \n* Gym-style Interface.\n\"\" \n\n* Support ROS. \n\nQuickstart example code to use this benckmark.\n\n import env\n maze0 = env.GazeboMaze(maze_id=0, continuous=True)\n observation = maze0.reset()\n done = False\n while not done:\n # Stochastic strategy\n action = dict()\n action['linear_vel'] = np.random.uniform(0, 1)\n action['angular_vel'] = np.random.uniform(-1, 1)\n observation, done, reward = maze0.execute(action)\n print(action, reward)\n maze0.close()\n \n## 1. Memorizing\n\"\" \n\n### VAE-based planner\n#### VAE Structure and Training\nThe designed VAE strcture is shown in the lower left figure. Train it in maze1 and maze2. The kl_tolerace is set to 0.5 (We stop optimizing for KL loss term once it is lower than some level, rather than letting it go to near zero) and latent dim is 32, thus the total loss is trained as close as possible to 16.\n\n\"\" \"\"
\nThe following results are tested in maze3 to verify the ability of generalization.\n\"\" \n\n#### Planner Structure \nVAE-based planner & Baseline network structure
\n\"\" \"\" \n\n### Performance\n\n1. The proposed trajectory is blue and the baseline is green.
\n\"\"
\n2. The success rate comparision in maze1.
\n\"\" \n\n3. Performance comparision
\n\n | SPL | Benchmark | Proposed |\n |---------|-------------|-----------|\n | maze1 | 0.702 | 0.703 |\n | maze2 | 0.611 | 0.626 |\n\nThat is, the proposed motion planner not only has much better sample-efficience, but also it has better performance. Actually, the shortest path in two mazes are both found by proposed motion planner (26 timesteps in maze1 and 29 time steps in maze2 with acceleration in simulation).\n\n## 2. From Memorizing to Reasoning\n\"\" \n\n### Stacked LSTM and network structure\nStacked LSTM
\n\"\"
\nnetwork structure
\n\"\" \n### Result\nSuccess rate in maze1
\n\"\" \n\n## Install\n#### Ddependencies\ntensorflow: 1.5.0
\nOS: Ubuntu 16.04
\nPython: 2.7
\nOpenCV: 3
\nROS: Kinetic
\nGazebo: 7
\ntensorforce: https://github.com/tensorforce/tensorforce
\n\n # install tensorflow-gpu after cudnn and cuda are installed\n pip install tensorflow-gpu==1.5.0\n # or just use tensorflow-cpu if no Nvidia GPU, it can also work.\n pip install tensorflow==1.5.0\n # install OpenCV: https://docs.opencv.org/master/d7/d9f/tutorial_linux_install.html\n # install ROS: http://wiki.ros.org/kinetic/Installation/Ubuntu\n # install Gazebo \n sudo apt-get install gazebo7 libgazebo7-dev\n # install old version that supports python2 of tensorforce form source\n \n#### Run\n sudo apt-get install ros-kinetic-gazebo-ros-pkgs ros-kinetic-gazebo-ros-control\n sudo apt-get install ros-kinetic-turtlebot-*\n sudo apt-get remove ros-kinetic-turtlebot-description\n sudo apt-get install ros-kinetic-kobuki-description\n # change to catkin_ws/src\n git clone https://github.com/marooncn/navbot\n cd ..\n catkin_make\n source ./devel/setup.bash\n # you can change the configure in config.py\n cd src/navbot/rl_nav/scripts\n # run the proposed model for memorizing\n python PPO.py\n # run the proposed model for reasoning\n python E2E_PPO_rnn.py\n#### Details\n1. The default environment is maze1, you need to change maze_id in [nav_gazebo.launch](https://github.com/marooncn/navbot/blob/master/rl_nav/launch/nav_gazebo.launch) and [config.py](https://github.com/marooncn/navbot/blob/master/rl_nav/scripts/config.py) if you want change the environment.
\n2. To execute [01_generate_data.py](https://github.com/marooncn/navbot/blob/master/rl_nav/scripts/worldModels/01_generate_data.py) to generate data, you need to comment the goal-related code in [nav_gazebo.launch](https://github.com/marooncn/navbot/blob/master/rl_nav/launch/nav_gazebo.launch) and [env.py](https://github.com/marooncn/navbot/blob/master/rl_nav/scripts/env.py).
\n3. maze1 and maze2 are speeded up 10 times to train, if you want speed up other environments, just change \n\n 0.001\n 1\n to\n \n 0.01\n \n in the environment file in [worlds](https://github.com/marooncn/navbot/tree/master/rl_nav/worlds).\n4. To reproduce the result, please change the related parameters in [config.py](https://github.com/marooncn/navbot/blob/master/rl_nav/scripts/config.py) according to [config.txt](https://github.com/marooncn/navbot/blob/master/materials/record/config.txt).\n5. PPO is not a deterministic policy gradient algorithm, the action at every timestep is sampled according to the distribution. It can be seen as \"noise\" and it's useful for explorations and generalizations. If you want to use the best strategy after the model is trained, just change 'deterministic = True' in [config.py](https://github.com/marooncn/navbot/blob/master/rl_nav/scripts/config.py) and the performance will be improved.\n\n## Cite\nIf your find the work is helpful in your research, please cite the following papers:\n* [Using RGB Image as Visual Input for Mapless Robot Navigation](https://arxiv.org/ftp/arxiv/papers/1903/1903.09927.pdf) \n* [Learning to Navigate in Indoor Environments: from Memorizing to Reasoning](https://arxiv.org/ftp/arxiv/papers/1904/1904.06933.pdf)\n\n## Blog\n[Introduction to tensorforce](https://mp.weixin.qq.com/s?__biz=Mzg2MjExNjY5Mg==&mid=2247483685&idx=1&sn=c73822b5b719db40648700d4242499e3&chksm=ce0d8f1ef97a06082fd9032b42d8699bc19adce339f2435f5b6a61c0ea9beb1cfc926509a8e0&mpshare=1&scene=1&srcid=&pass_ticket=9Mwfi8nrJduWesFYZOvfaN1uXqSrd%2B2CuQl%2FzqbUNmBAfv%2Bx%2BxgJyw8xSQfYkcsl#rd)(Chinese)
\n[Introduction to this work](https://mp.weixin.qq.com/s?__biz=Mzg2MjExNjY5Mg==&mid=2247483714&idx=1&sn=449c6c1b00272d31b9093e8ae32e5ca5&chksm=ce0d8f79f97a066fcc5929cdbd0fc83ce8412eaf9d97a5c51ed16799d7e8a401027dc3bb6486&mpshare=1&scene=1&srcid=&pass_ticket=9Mwfi8nrJduWesFYZOvfaN1uXqSrd%2B2CuQl%2FzqbUNmBAfv%2Bx%2BxgJyw8xSQfYkcsl#rd)(Chinese)
\n\n## Reference\n[tensorforce](https://github.com/tensorforce/tensorforce)([blog](https://www.jiqizhixin.com/articles/2017-07-14-7?from=synced&keyword=tensorforce))
\n[gym_gazebo](https://github.com/erlerobot/gym-gazebo/blob/master/gym_gazebo/envs/turtlebot/gazebo_circuit2c_turtlebot_camera_nn.py)
\n[gazebo](http://wiki.ros.org/gazebo)
\n[roslaunch python API](http://wiki.ros.org/roslaunch/API%20Usage)
\n[turtlebot_description](https://github.com/turtlebot/turtlebot/tree/kinetic/turtlebot_description)
\n[kobuki_description](https://github.com/yujinrobot/kobuki/tree/devel/kobuki_description)
\n[WorldModelsExperiments(official)](https://github.com/hardmaru/WorldModelsExperiments)
\n[WorldModels(by Applied Data Science)](https://github.com/AppliedDataSciencePartners/WorldModels)
\n" }, { "path": "materials/record/E2E_PPO_nav1.txt", "content": "[115.47596913301666, 25, False]\n[8.994252478777824, 9, False]\n[-9.1136486896348, 10, False]\n[3.262886654290625, 10, False]\n[0.16921281397247867, 6, False]\n[-30.686545091661326, 16, False]\n[24.456119274280965, 9, False]\n[-2.787359278922139, 6, False]\n[59.224686010771364, 18, False]\n[22.648249691124335, 8, False]\n[-49.113378949444574, 7, False]\n[175.3922422186731, 17, False]\n[111.27832309689003, 27, False]\n[-8.130274552407997, 8, False]\n[15.615689492362748, 7, False]\n[18.54498663610346, 6, False]\n[30.58141583850022, 12, False]\n[20.974246912145745, 9, False]\n[-4.342488452540167, 7, False]\n[59.457486098920995, 17, False]\n[89.07285596639878, 31, False]\n[-3.5039485949718454, 7, False]\n[7.6452015888657385, 7, False]\n[79.87659865713636, 26, False]\n[35.524719805432355, 7, False]\n[35.53042891286832, 7, False]\n[197.88850695303802, 26, False]\n[15.744336827534525, 7, False]\n[161.04232836326756, 19, 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False]\n[176.5882672426859, 37, False]\n[171.7885042007031, 26, False]\n[448.076508060003, 47, True]\n[167.78406680000464, 29, False]\n[448.268790342814, 42, True]\n[453.19348474064856, 36, True]\n[453.07731460118714, 30, True]\n[169.424318416343, 30, False]\n[159.20431554004796, 39, False]\n[-7.269965579508131, 7, False]\n[66.42147469254867, 18, False]\n[169.11900370163585, 38, False]\n[452.57009098254105, 38, True]\n[453.0356107132382, 35, True]\n[168.13194070936436, 26, False]\n[452.2136877968541, 39, True]\n[171.02343889342626, 27, False]\n[449.436746982556, 38, True]\n[91.97078975630856, 20, False]\n[167.79954758716494, 21, False]\n[451.96568821093604, 37, True]\n[452.1730915738176, 42, True]\n[451.7514889232683, 45, True]\n[452.67330576174703, 49, True]\n[88.94164873004044, 18, False]\n[452.7559160174532, 33, True]\n[451.6375471710627, 43, True]\n[452.80308230382036, 33, True]\n[444.4464184857261, 43, True]\n[451.3845604090827, 35, True]\n[452.4485536595563, 37, 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True]\n[453.03501341602197, 34, True]\n[449.845186673316, 33, True]\n[452.36008372619165, 32, True]\n[451.27441114124844, 38, True]\n[448.89989393338146, 38, True]\n[93.90902132819676, 21, False]\n[18.356577775472672, 10, False]\n[447.249061526042, 35, True]\n[78.67435139423688, 16, False]\n[452.452414463292, 36, True]\n[168.71399205631744, 28, False]\n[451.56304013736315, 48, True]\n[241.21599850884093, 72, False]\n[452.26423408053887, 34, True]\n[452.6194917391295, 43, True]\n[236.8021010736722, 74, False]\n[452.6642636036287, 41, True]\n[448.92372821714497, 48, True]\n[453.3505973984235, 38, True]\n[446.8495624405289, 35, True]\n[77.12611119929497, 18, False]\n[70.20494667259595, 17, False]\n[452.98841671215285, 41, True]\n[452.68350529311704, 48, True]\n[445.2688597441207, 31, True]\n[452.4345229864273, 41, True]\n[450.76506722938205, 38, True]\n[451.9437877094551, 48, True]\n[451.25780479190456, 44, True]\n[453.50965485001484, 31, True]\n[446.3204750582338, 30, True]\n[174.5478473210694, 35, False]\n[71.64843264695541, 23, False]\n[9.511899418557796, 8, False]\n[236.66365148356635, 79, False]\n[450.3028018658658, 34, True]\n[450.378273319106, 44, True]\n[444.1196202616547, 36, True]\n[446.2963703829974, 32, True]\n[451.48653859403845, 44, True]\n[453.27201775734727, 38, True]\n[452.08670240424124, 33, True]\n[450.44914699399874, 46, True]\n[453.10940336657205, 37, True]\n[449.3599826516374, 32, True]\n[451.1408448337369, 36, True]\n[170.07624873815524, 26, False]\n[452.1790392528637, 38, True]\n[453.2388873760202, 34, True]\n[451.99226945390086, 37, True]\n[447.8229714608468, 36, True]\n[453.31475183169664, 37, True]\n[450.14741324790845, 44, True]\n" }, { "path": "materials/record/config.txt", "content": "################################## parameters of task 1: memorizing ##################################\n# reward parameter\nr_arrive = 100\nr_collision = -50 \nCr = 100\nCp = -0.05 # time step penalty\n# goal\ngoal_space = []\nstart_space = []\ntest_space = []\ngoal_space_nav0 = []\ngoal_space_nav1 = [[-3.8, -3.8]]\ngoal_space_nav2 = [[-3, -3.2]]\n\nstart_space_nav0 = []\nstart_space_nav1 = [[-0.4, -2.6]] # ,4.0/3*math.pi, set 'theta = 4.0/3*math.pi' in reset function of env.py\nstart_space_nav2 = [[0.65, -3.0]] # ,1.0/2*math.pi, set 'theta = 1.0/2*math.pi' in reset function of env.py\n\ngoal_space.append(goal_space_nav0)\ngoal_space.append(goal_space_nav1)\ngoal_space.append(goal_space_nav2)\n\nstart_space.append(start_space_nav0)\nstart_space.append(start_space_nav1)\nstart_space.append(start_space_nav2)\n# step, every step sleep 0.05s\nCd = 0.1 \n# max linear velocity\nv_max = 0.5 # m/s\n# max angular velocity\nw_max = 1.2 # rad/s\n\n# VAE related\nlatent_vector_dim = 32\nvae_weight = path + '/scripts/worldModels/models/vae_weights.h5'\n\n########################## parameters of task 2: from memorizing to reasoning ###########################\n# reward parameter\nr_arrive = 100\nr_collision = -50 \nCr = 100\nCp = -0.05 # time step penalty\n# goal\ngoal_space = []\nstart_space = []\ntest_space = []\ngoal_space_nav0 = []\ntest_space_nav0 = []\ngoal_space_nav1 = [[-3.8, -3.8], [-1.5, -4], [-3, -2.2]]\ntest_space_nav1 = [goal_space_nav1[-1]]\ngoal_space_nav2 = [[-3, -3.2], [-1.8, -3.2], [-2, -2.6], [-0.8, -3.5], [-0.4, -2.6], [-2.5, -3], [-3, -3], [-2, -1.9],\n [-1.3, -3.8], [-3.3, -4], [-3, -3.8], [-1.2, -3.2]]\ntest_space_nav2 = [goal_space_nav2[-1], goal_space_nav2[-2]]\nstart_space_nav0 = []\nstart_space_nav1 = [[-0.4, -2.6]] # 4.0/3*math.pi,set 'theta = 4.0/3*math.pi' in reset function of env.py\nstart_space_nav2 = [[0.65, -3.0]] # ,1.0/2*math.pi, set 'theta = 1.0/2*math.pi' in reset function of env.py\ngoal_space.append(goal_space_nav0)\ngoal_space.append(goal_space_nav1)\ngoal_space.append(goal_space_nav2)\ntest_space.append(test_space_nav0)\ntest_space.append(test_space_nav1)\ntest_space.append(test_space_nav2)\nstart_space.append(start_space_nav0)\nstart_space.append(start_space_nav1)\nstart_space.append(start_space_nav2)\n\nCd = 0.354/2 # =r \n# max linear velocity\nv_max = 0.3 # m/s\n# max angular velocity\nw_max = 1.0 # rad/s\n\n" }, { "path": "notes.md", "content": "### Basics\n#### RL\nMy paper reading [notes](https://github.com/marooncn/learning_note/blob/master/paper%20reading/Reinforcement%20Learning.md) of Reinforcement Learning.\n#### Simutation Environment\n[gym-gazebo](https://github.com/erlerobot/gym-gazebo)(Erle Robotics 2016)
\n[DeepMind Lab](https://github.com/deepmind/lab)(DeepMind 2016)
\n[SUNGG](http://suncg.cs.princeton.edu/)(Princeton 2017)
\n[Matterport3D](https://github.com/niessner/Matterport)(Princeton etc 2017)
\n[AI2-THOR](https://github.com/allenai/ai2thor)(AI2 2017)
\n[House3D](https://github.com/facebookresearch/House3D)(facebook 2018)
\n[Gibson Env](https://github.com/StanfordVL/GibsonEnv)(Stanford 2018)
\n##### Comparision \n\"simulation
\n\"
\n[Habitat](https://github.com/facebookresearch/habitat-sim)(Facebook 2019, big gays)
\n\n### Papers \n[Building Generalizable Agents with a Realistic and Rich 3D Environment](https://arxiv.org/pdf/1801.02209.pdf)\n\"DDPG
\n* Environment
\nHouse3D\n* Success measure
\nTo declare success, we want to ensure that the agent\nidentifies the target room by its unique properties (e.g. presence of appropriate objects in the room such as pan and knives for kitchen and bed for bedroom) instead of merely reaching there by luck. An episode is considered successful if both of the following two criteria are satisfied: (1) the agent\nis located inside the target room; (2) the agent consecutively sees a designated object category associated with that target room type for at least 2 time steps. We assume that an agent sees an object if there are at least 4% of pixels in X belonging to that object.\n* Reward
\n-0.1(collision penalty) -0.1*timestep +10(success reward)
\noriginal reward is too sparse, do reward shaping for each step: the difference of shortest distances between the agent's movement.\n* result
\nOur final gated-LSTM agent achieves a success rate of 35.8% on 50 unseen environments\n\n\n[On Evaluation of Embodied Navigation Agents](https://arxiv.org/pdf/1807.06757.pdf)(arxiv 2018)
\n\n### Agent architecture\n#### purely reactive sensory input \n(sensory input-> DNN -> action)
\n[learning to act by predicting the future](https://arxiv.org/pdf/1611.01779.pdf)([code](https://github.com/IntelVCL/DirectFuturePrediction), ICLR 2017)
\n[Target-driven visual navigation in indoor scenes using deep reinforcement learning](https://arxiv.org/pdf/1609.05143.pdf)([code](https://github.com/yushu-liu/icra2017-visual-navigation), ICRA 2017)
\n[Visual semantic navigation using scene priors](https://arxiv.org/pdf/1810.06543.pdf)(ICLR 2019)
\n#### equip with short-term memory\n[reinforcement learning with unsupervised auxiliary tasks](https://arxiv.org/pdf/1611.05397.pdf)([code](https://github.com/miyosuda/unreal), ICLR 2017)
\n[Playing FPS Games with Deep Reinforcement Learning](https://arxiv.org/pdf/1609.05521.pdf)([code](https://github.com/glample/Arnold), AAAI 2017)
\n[Learning to navigate in complex environments](https://arxiv.org/pdf/1611.03673.pdf)(ICLR 2017)
\n[Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning](https://arxiv.org/pdf/1805.01956.pdf)([code](https://github.com/mfe7/cadrl_ros)(IROS 2018, MIT)
\n#### internal representations\n(use more advanced memory mechanisms that support the construction of rich internal representations of the agent's environment)
\n[Control of memory, active perception, and action in Minecraft](https://web.eecs.umich.edu/~baveja/Papers/ICML2016.pdf)(ICML 2016)
\n[Cognitive mapping and planning for visual navigation](https://arxiv.org/pdf/1702.03920.pdf)(CVPR 2017)
\n[Unifying map and landmark based representations for visual navigation](https://arxiv.org/pdf/1712.08125.pdf)(arXiv 2017)
\n[Neural map: Structured memory for deep reinforcement learning](https://arxiv.org/pdf/1702.08360.pdf)(ICLR 2018)
\n[Semi-parametric topological memory for navigation](https://arxiv.org/pdf/1803.00653.pdf)([code](https://github.com/nsavinov/SPTM), ICLR 2018)
\n#### Meta-Learning\n[Learning to Learn How to Learn: Self-Adaptive Visual Navigation using Meta-Learning](https://arxiv.org/pdf/1812.00971.pdf)([code](https://github.com/allenai/savn), CVPR 2019)
\n" }, { "path": "rl_nav/CMakeLists.txt", "content": "cmake_minimum_required(VERSION 2.8.3)\nproject(rl_nav)\n\n## Compile as C++11, supported in ROS Kinetic and newer\n# add_compile_options(-std=c++11)\n\n## Find catkin macros and libraries\n## if COMPONENTS list like find_package(catkin REQUIRED COMPONENTS xyz)\n## is used, also find other catkin packages\nfind_package(catkin REQUIRED)\n\n## System dependencies are found with CMake's conventions\n# find_package(Boost REQUIRED COMPONENTS system)\n\n\n## Uncomment this if the package has a setup.py. This macro ensures\n## modules and global scripts declared therein get installed\n## See http://ros.org/doc/api/catkin/html/user_guide/setup_dot_py.html\n# catkin_python_setup()\n\n################################################\n## Declare ROS messages, services and actions ##\n################################################\n\n## To declare and build messages, services or actions from within this\n## package, follow these steps:\n## * Let MSG_DEP_SET be the set of packages whose message types you use in\n## your messages/services/actions (e.g. std_msgs, actionlib_msgs, ...).\n## * In the file package.xml:\n## * add a build_depend tag for \"message_generation\"\n## * add a build_depend and a exec_depend tag for each package in MSG_DEP_SET\n## * If MSG_DEP_SET isn't empty the following dependency has been pulled in\n## but can be declared for certainty nonetheless:\n## * add a exec_depend tag for \"message_runtime\"\n## * In this file (CMakeLists.txt):\n## * add \"message_generation\" and every package in MSG_DEP_SET to\n## find_package(catkin REQUIRED COMPONENTS ...)\n## * add \"message_runtime\" and every package in MSG_DEP_SET to\n## catkin_package(CATKIN_DEPENDS ...)\n## * uncomment the add_*_files sections below as needed\n## and list every .msg/.srv/.action file to be processed\n## * uncomment the generate_messages entry below\n## * add every package in MSG_DEP_SET to generate_messages(DEPENDENCIES ...)\n\n## Generate messages in the 'msg' folder\n# add_message_files(\n# FILES\n# Message1.msg\n# Message2.msg\n# )\n\n## Generate services in the 'srv' folder\n# add_service_files(\n# FILES\n# Service1.srv\n# Service2.srv\n# )\n\n## Generate actions in the 'action' folder\n# add_action_files(\n# FILES\n# Action1.action\n# Action2.action\n# )\n\n## Generate added messages and services with any dependencies listed here\n# generate_messages(\n# DEPENDENCIES\n# std_msgs # Or other packages containing msgs\n# )\n\n################################################\n## Declare ROS dynamic reconfigure parameters ##\n################################################\n\n## To declare and build dynamic reconfigure parameters within this\n## package, follow these steps:\n## * In the file package.xml:\n## * add a build_depend and a exec_depend tag for \"dynamic_reconfigure\"\n## * In this file (CMakeLists.txt):\n## * add \"dynamic_reconfigure\" to\n## find_package(catkin REQUIRED COMPONENTS ...)\n## * uncomment the \"generate_dynamic_reconfigure_options\" section below\n## and list every .cfg file to be processed\n\n## Generate dynamic reconfigure parameters in the 'cfg' folder\n# generate_dynamic_reconfigure_options(\n# cfg/DynReconf1.cfg\n# cfg/DynReconf2.cfg\n# )\n\n###################################\n## catkin specific configuration ##\n###################################\n## The catkin_package macro generates cmake config files for your package\n## Declare things to be passed to dependent projects\n## INCLUDE_DIRS: uncomment this if your package contains header files\n## LIBRARIES: libraries you create in this project that dependent projects also need\n## CATKIN_DEPENDS: catkin_packages dependent projects also need\n## DEPENDS: system dependencies of this project that dependent projects also need\ncatkin_package(\n# INCLUDE_DIRS include\n# LIBRARIES rl_baseline\n# CATKIN_DEPENDS other_catkin_pkg\n# DEPENDS system_lib\n)\n\n###########\n## Build ##\n###########\n\n## Specify additional locations of header files\n## Your package locations should be listed before other locations\ninclude_directories(\n# include\n# ${catkin_INCLUDE_DIRS}\n)\n\n## Declare a C++ library\n# add_library(${PROJECT_NAME}\n# src/${PROJECT_NAME}/rl_baseline.cpp\n# )\n\n## Add cmake target dependencies of the library\n## as an example, code may need to be generated before libraries\n## either from message generation or dynamic reconfigure\n# add_dependencies(${PROJECT_NAME} ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})\n\n## Declare a C++ executable\n## With catkin_make all packages are built within a single CMake context\n## The recommended prefix ensures that target names across packages don't collide\n# add_executable(${PROJECT_NAME}_node src/rl_baseline_node.cpp)\n\n## Rename C++ executable without prefix\n## The above recommended prefix causes long target names, the following renames the\n## target back to the shorter version for ease of user use\n## e.g. \"rosrun someones_pkg node\" instead of \"rosrun someones_pkg someones_pkg_node\"\n# set_target_properties(${PROJECT_NAME}_node PROPERTIES OUTPUT_NAME node PREFIX \"\")\n\n## Add cmake target dependencies of the executable\n## same as for the library above\n# add_dependencies(${PROJECT_NAME}_node ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})\n\n## Specify libraries to link a library or executable target against\n# target_link_libraries(${PROJECT_NAME}_node\n# ${catkin_LIBRARIES}\n# )\n\n#############\n## Install ##\n#############\n\n# all install targets should use catkin DESTINATION variables\n# See http://ros.org/doc/api/catkin/html/adv_user_guide/variables.html\n\n## Mark executable scripts (Python etc.) for installation\n## in contrast to setup.py, you can choose the destination\n# install(PROGRAMS\n# scripts/my_python_script\n# DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}\n# )\n\n## Mark executables and/or libraries for installation\n# install(TARGETS ${PROJECT_NAME} ${PROJECT_NAME}_node\n# ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}\n# LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}\n# RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}\n# )\n\n## Mark cpp header files for installation\n# install(DIRECTORY include/${PROJECT_NAME}/\n# DESTINATION ${CATKIN_PACKAGE_INCLUDE_DESTINATION}\n# FILES_MATCHING PATTERN \"*.h\"\n# PATTERN \".svn\" EXCLUDE\n# )\n\n## Mark other files for installation (e.g. launch and bag files, etc.)\n# install(FILES\n# # myfile1\n# # myfile2\n# DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION}\n# )\n\n#############\n## Testing ##\n#############\n\n## Add gtest based cpp test target and link libraries\n# catkin_add_gtest(${PROJECT_NAME}-test test/test_rl_baseline.cpp)\n# if(TARGET ${PROJECT_NAME}-test)\n# target_link_libraries(${PROJECT_NAME}-test ${PROJECT_NAME})\n# endif()\n\n## Add folders to be run by python nosetests\n# catkin_add_nosetests(test)\n" }, { "path": "rl_nav/launch/nav_gazebo.launch", "content": "\n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n \n \n\n \n \n\n \n \n \n \n\n \n \n \n \n \n\n \n\n\n\n\n\n" }, { "path": "rl_nav/launch/show_all.launch", "content": "\n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n \n \n\n\n \n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n\n" }, { "path": "rl_nav/launch/spawn_goal.launch", "content": "\n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n\n \n\n \n\n\n" }, { "path": "rl_nav/package.xml", "content": "\n\n rl_nav\n 0.0.0\n The rl_nav package\n\n \n \n \n maroon\n\n\n \n \n \n TODO\n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n catkin\n\n\n \n \n \n\n \n\n" }, { "path": "rl_nav/scripts/DQN.py", "content": "from tensorforce.agents import DQNAgent\nimport os\nimport itertools\n\nimport config\nimport env\nimport worldModels.VAE\n\nmaze_id = config.maze_id\nrestore = False\n\nGazeboMaze = env.GazeboMaze(maze_id=maze_id, continuous=False)\n\nrecord_dir = 'record'\nif not os.path.exists(record_dir):\n os.makedirs(record_dir)\n\nsaver_dir = './models/nav{}'.format(maze_id)\nif not os.path.exists(saver_dir):\n os.makedirs(saver_dir)\n\nsummarizer_dir = './record/DQN/nav{}'.format(maze_id)\nif not os.path.exists(summarizer_dir):\n os.makedirs(summarizer_dir)\n\nvae = worldModels.VAE.VAE()\nvae.set_weights(config.vae_weight)\n\n\n# Network as list of layers\nnetwork_spec = [\n dict(type='dense', size=512, activation='relu'),\n dict(type='dense', size=512, activation='relu'),\n dict(type='dense', size=512, activation='relu')]\n\n\nmemory = dict(\n type='replay',\n include_next_states=True,\n capacity=30000\n)\n\nexploration = dict(\n type='epsilon_decay',\n initial_epsilon=1.0,\n final_epsilon=1e-1,\n timesteps=5000000,\n start_timestep=0\n)\n\nupdate_model = dict(\n unit='timesteps',\n # 64 timesteps per update\n batch_size=32,\n # Every 64 timesteps\n frequency=32\n)\n\noptimizer = dict(\n type='adam',\n learning_rate=0.0001\n)\n\n# Instantiate a Tensorforce agent\nagent = DQNAgent(\n states=dict(shape=(36,), type='float'), # GazeboMaze.states,\n actions=GazeboMaze.actions,\n network=network_spec,\n update_mode=update_model,\n memory=memory,\n actions_exploration=exploration,\n optimizer=optimizer,\n saver=dict(directory=saver_dir, basename='DQN_model.ckpt', load=restore, seconds=10800),\n summarizer=dict(directory=summarizer_dir, labels=[\"graph\", \"losses\", \"reward\"], seconds=10800),\n double_q_model=True\n)\n\n\nepisode = 0\nmax_timesteps = 1000\nmax_episodes = 10000\nepisode_rewards = []\nsuccesses = []\n\n\nwhile True:\n observation = GazeboMaze.reset()\n observation = observation / 255.0 # normalize\n agent.reset()\n\n timestep = 0\n episode_reward = 0\n success = False\n action = -1\n\n while True:\n latent_vector = vae.get_vector(observation.reshape(1, 48, 64, 3))\n latent_vector = list(itertools.chain(*latent_vector)) # [[ ]] -> [ ]\n relative_pos = GazeboMaze.p\n previous_act = GazeboMaze.vel_cmd\n state = latent_vector + relative_pos + previous_act\n\n # Query the agent for its action decision\n action = agent.act(state)\n print(action)\n # Execute the decision and retrieve the current information\n observation, terminal, reward = GazeboMaze.execute(action)\n observation = observation / 255.0 # normalize\n # print(reward)\n # Pass feedback about performance (and termination) to the agent\n agent.observe(terminal=terminal, reward=reward)\n timestep += 1\n episode_reward += reward\n if terminal or timestep == max_timesteps:\n success = GazeboMaze.success\n break\n\n episode += 1\n # avg_reward = float(episode_reward)/timestep\n successes.append(success)\n episode_rewards.append([episode_reward, timestep, success])\n\n # print('{} episode total reward: {}'.format(episode, episode_reward))\n\n if episode % 1000 == 0:\n f = open(record_dir + '/DQN_episode' + str(episode) + '.txt', 'w')\n for i in episode_rewards:\n f.write(str(i))\n f.write('\\n')\n f.close()\n episode_rewards = []\n agent.save_model('./models/')\n\n if len(successes) > 100:\n if sum(successes[-100:]) > 90:\n GazeboMaze.close()\n agent.save_model('./models/')\n break\n\n # if episode == max_episodes:\n # break\n" }, { "path": "rl_nav/scripts/E2E_DDPG_rnn.py", "content": "from tensorforce.agents import DDPGAgent\nimport os\nimport env\nimport config\n\n# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\nmaze_id = config.maze_id\nrestore = False\n\nGazeboMaze = env.GazeboMaze(maze_id=maze_id, continuous=True)\n\nrecord_dir = 'record'\nif not os.path.exists(record_dir):\n os.makedirs(record_dir)\n\nsaver_dir = './models/nav{}'.format(maze_id)\nif not os.path.exists(saver_dir):\n os.makedirs(saver_dir)\n\nsummarizer_dir = './record/E2E_DDPG_rnn/nav{}'.format(maze_id)\nif not os.path.exists(summarizer_dir):\n os.makedirs(summarizer_dir)\n\nstates = dict(\n image=GazeboMaze.states,\n relative_pos=dict(shape=(2,), type='float'),\n previous_act=dict(shape=(2,), type='float'),\n previous_reward=dict(shape=(1,), type='float')\n)\n\n\nnetwork_spec = [\n [\n dict(type='input', names=['image']),\n dict(type='conv2d', size=32, window=(8, 6), stride=4, activation='relu', padding='SAME'),\n dict(type='conv2d', size=64, window=(4, 3), stride=2, activation='relu', padding='SAME'),\n dict(type='pool2d', pooling_type='max', window=2, stride=2, padding='SAME'),\n dict(type='conv2d', size=64, window=2, stride=2, activation='relu', padding='SAME'),\n dict(type='flatten'),\n dict(type='dense', size=32, activation='relu'),\n dict(type='output', name='image_output')\n ],\n [\n dict(type='input', names=['image_output', 'relative_pos'], aggregation_type='concat'),\n dict(type='internal_lstm', size=256),\n dict(type='output', name='lstm_output'),\n ],\n\n [\n dict(type='input', names=['lstm_output', 'previous_act', 'previous_reward'], aggregation_type='concat'),\n dict(type='internal_lstm', size=256),\n dict(type='dense', size=256, activation='relu'),\n ]\n\n]\n\ncritic_network_spec = [\n [\n dict(type='input', names=['image']),\n dict(type='conv2d', size=32, window=(8, 6), stride=4, activation='relu', padding='SAME'),\n dict(type='conv2d', size=64, window=(4, 3), stride=2, activation='relu', padding='SAME'),\n dict(type='pool2d', pooling_type='max', window=2, stride=2, padding='SAME'),\n dict(type='conv2d', size=128, window=2, stride=2, activation='relu', padding='SAME'),\n dict(type='flatten'),\n dict(type='output', name='image_output')\n ],\n [\n dict(type='input', names=['image_output', 'relative_pos', 'angular_vel', 'linear_vel'],\n aggregation_type='concat'),\n dict(type='dense', size=256, activation='relu'),\n dict(type='dense', size=128, activation='relu'),\n dict(type='output', name='output2')\n ],\n [\n dict(type='input', names=['output2', 'previous_act', 'previous_reward'], aggregation_type='concat'),\n dict(type='dense', size=64, activation='relu'),\n dict(type='dense', size=1, activation='tanh')\n ]\n]\n\nmemory = dict(\n type='replay',\n include_next_states=True,\n capacity=10000\n)\n\nexploration = dict(\n type='epsilon_decay',\n initial_epsilon=1.0,\n final_epsilon=0.1,\n timesteps=100000,\n start_timestep=0\n)\n\noptimizer = dict(\n type='adam',\n learning_rate=0.0001\n)\n\ncritic_optimizer = dict(\n type='adam',\n learning_rate=0.001\n)\n\n# Instantiate a Tensorforce agent\nagent = DDPGAgent(\n states=states,\n actions=GazeboMaze.actions,\n network=network_spec,\n critic_network=critic_network_spec,\n # update_mode=update_model,\n # memory=memory,\n actions_exploration=exploration,\n saver=dict(directory=saver_dir, basename='E2E_DDPG_rnn_model.ckpt', load=restore, seconds=6000),\n summarizer=dict(directory=summarizer_dir, labels=[\"graph\", \"losses\", \"reward\", \"entropy\"], seconds=6000),\n optimizer=optimizer,\n critic_optimizer=critic_optimizer\n)\n\n\nepisode = 0\ntest_episode = 0\ntotal_timestep = 0\nmax_timesteps = 1000\nmax_episodes = 10000\nepisode_rewards = []\ntest_rewards = []\nsuccesses = []\ntest_successes = []\n\nwhile True:\n agent.reset()\n observation = GazeboMaze.reset()\n observation = observation / 255.0 # normalize\n\n timestep = 0\n episode_reward = 0\n success = False\n\n if GazeboMaze.goal not in config.test_space[maze_id]: # train\n while True:\n relative_pos = GazeboMaze.p\n previous_act = GazeboMaze.vel_cmd\n previous_reward = GazeboMaze.reward\n print(previous_act)\n state = dict(image=observation, relative_pos=relative_pos,\n previous_act=previous_act, previous_reward=[previous_reward])\n # state = dict(image=observation, previous_act=GazeboMaze.vel_cmd, relative_pos=GazeboMaze.p)\n\n # Query the agent for its action decision\n action = agent.act(state)\n # Execute the decision and retrieve the current information\n observation, terminal, reward = GazeboMaze.execute(action)\n observation = observation / 255.0 # normalize\n # print(reward)\n # Pass feedback about performance (and termination) to the agent\n agent.observe(terminal=terminal, reward=reward)\n timestep += 1\n episode_reward += reward\n if terminal or timestep == max_timesteps:\n success = GazeboMaze.success\n break\n\n episode += 1\n total_timestep += timestep\n # avg_reward = float(episode_reward)/timestep\n episode_rewards.append([episode_reward, timestep, success])\n\n # if total_timestep > 100000:\n # print('{}th episode reward: {}'.format(episode, episode_reward))\n\n if episode % 1000 == 0:\n f = open(record_dir + '/E2E_DDPG_rnn_nav{}'.format(maze_id) + '.txt', 'a+')\n for i in episode_rewards:\n f.write(str(i))\n f.write('\\n')\n f.close()\n episode_rewards = []\n agent.save_model('./models/')\n\n else: # test\n while True:\n relative_pos = GazeboMaze.p\n previous_act = GazeboMaze.vel_cmd\n previous_reward = GazeboMaze.reward\n print(previous_act)\n state = dict(image=observation, relative_pos=relative_pos,\n previous_act=previous_act, previous_reward=[previous_reward])\n # state = dict(image=observation, previous_act=GazeboMaze.vel_cmd, relative_pos=GazeboMaze.p)\n\n # Query the agent for its action decision\n action = agent.act(state, independent=True)\n # Execute the decision and retrieve the current information\n observation, terminal, reward = GazeboMaze.execute(action)\n observation = observation / 255.0 # normalize\n # print(reward)\n # Pass feedback about performance (and termination) to the agent\n # agent.observe(terminal=terminal, reward=reward)\n timestep += 1\n episode_reward += reward\n if terminal or timestep == max_timesteps:\n success = GazeboMaze.success\n break\n\n test_episode += 1\n test_successes.append(success)\n test_rewards.append([episode_reward, timestep, success])\n\n if test_episode % 100 == 0:\n f = open(record_dir + '/E2E_DDPG_rnn_test_nav{}'.format(maze_id) + '.txt', 'a+')\n for i in test_rewards:\n f.write(str(i))\n f.write('\\n')\n f.close()\n test_rewards = []\n\n\n if len(test_successes) > 100:\n if sum(test_successes[-100:]) > 60:\n GazeboMaze.close()\n agent.save_model('./models/')\n # save test data\n f = open(record_dir + '/E2E_DDPG_rnn_test_nav{}'.format(maze_id) + '.txt', 'a+')\n for i in test_rewards:\n f.write(str(i))\n f.write('\\n')\n f.close()\n # save training data\n f = open(record_dir + '/E2E_DDPG_rnn_nav{}'.format(maze_id) + '.txt', 'a+')\n for i in episode_rewards:\n f.write(str(i))\n f.write('\\n')\n f.close()\n print(\"Training End!\")\n break\n" }, { "path": "rl_nav/scripts/E2E_DQN.py", "content": "from tensorforce.agents import DQNAgent\nimport os\nimport env\nimport config\n\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n\nmaze_id = config.maze_id\nrestore = False\n\nGazeboMaze = env.GazeboMaze(maze_id=maze_id, continuous=False)\n\nrecord_dir = 'record'\nif not os.path.exists(record_dir):\n os.makedirs(record_dir)\n\nsaver_dir = './models/nav{}'.format(maze_id)\nif not os.path.exists(saver_dir):\n os.makedirs(saver_dir)\n\nsummarizer_dir = './record/E2E_DQN/nav{}'.format(maze_id)\nif not os.path.exists(summarizer_dir):\n os.makedirs(summarizer_dir)\n\nstates = dict(\n image=GazeboMaze.states,\n previous_act=dict(shape=(2,), type='float'),\n relative_pos=dict(shape=(2,), type='float')\n)\n\n\nnetwork_spec = [\n [\n dict(type='input', names=['image']),\n dict(type='conv2d', size=32, window=(8, 6), stride=4, activation='relu', padding='SAME'),\n dict(type='conv2d', size=64, window=(4, 3), stride=2, activation='relu', padding='SAME'),\n dict(type='pool2d', pooling_type='max', window=2, stride=2, padding='SAME'),\n dict(type='conv2d', size=64, window=2, stride=2, activation='relu', padding='SAME'),\n dict(type='flatten'),\n dict(type='output', name='image_output')\n ],\n [\n dict(type='input', names=['previous_act']),\n dict(type='dense', size=8, activation='relu'),\n dict(type='output', name='action_output'),\n ],\n [\n dict(type='input', names=['relative_pos']),\n dict(type='dense', size=16, activation='relu'),\n dict(type='output', name='position_output'),\n ],\n\n [\n dict(type='input', names=['image_output', 'action_output', 'position_output'], aggregation_type='concat'),\n dict(type='dense', size=512, activation='relu'),\n dict(type='dense', size=512, activation='relu'),\n\n ]\n\n]\n\nmemory = dict(\n type='replay',\n include_next_states=True,\n capacity=10000\n)\n\nexploration = dict(\n type='epsilon_decay',\n initial_epsilon=1.0,\n final_epsilon=0.1,\n timesteps=10000,\n start_timestep=0\n)\n\nupdate_model = dict(\n unit='timesteps',\n # 64 timesteps per update\n batch_size=64,\n # Every 64 timesteps\n frequency=64\n)\n\noptimizer = dict(\n type='adam',\n learning_rate=0.0001\n)\n\n# Instantiate a Tensorforce agent\nagent = DQNAgent(\n states=states,\n actions=GazeboMaze.actions,\n network=network_spec,\n # update_mode=update_model,\n # memory=memory,\n actions_exploration=exploration,\n saver=dict(directory='./models', basename='E2E_DQN_model.ckpt', load=restore, seconds=6000),\n summarizer=dict(directory='./record/E2E_DQN', labels=[\"graph\", \"losses\", \"reward\", \"'entropy'\"], seconds=6000),\n optimizer=optimizer,\n double_q_model=True\n)\n\n\nepisode = 0\nepisode_rewards = []\nsuccesses = []\ntotal_timestep = 0\nmax_timesteps = 1000\nmax_episodes = 50000\n\n\nwhile True:\n observation = GazeboMaze.reset()\n observation = observation / 255.0 # normalize\n agent.reset()\n\n timestep = 0\n episode_reward = 0\n success = False\n\n while True:\n state = dict()\n state['image'] = observation,\n state['previous_act'] = GazeboMaze.vel_cmd,\n state['relative_pos'] = GazeboMaze.p,\n # state = dict(image=observation, previous_act=GazeboMaze.vel_cmd, relative_pos=GazeboMaze.p)\n # print(state)\n\n # Query the agent for its action decision\n action = agent.act(state)\n print(action)\n # Execute the decision and retrieve the current information\n observation, terminal, reward = GazeboMaze.execute(action)\n observation = observation / 255.0 # normalize\n # print(reward)\n # Pass feedback about performance (and termination) to the agent\n agent.observe(terminal=terminal, reward=reward)\n timestep += 1\n episode_reward += reward\n if terminal or timestep == max_timesteps:\n success = GazeboMaze.success\n break\n\n episode += 1\n total_timestep += timestep\n # avg_reward = float(episode_reward)/timestep\n successes.append(success)\n episode_rewards.append([episode_reward, timestep, success])\n\n # if total_timestep > 100000:\n # print('{}th episode reward: {}'.format(episode, episode_reward))\n\n if episode % 100 == 0:\n f = open(record_dir + '/E2E_DQN_nav' + str(maze_id) + '.txt', 'a+')\n for i in episode_rewards:\n f.write(str(i))\n f.write('\\n')\n f.close()\n episode_rewards = []\n agent.save_model('./models/')\n\n if len(successes) > 100:\n if sum(successes[-100:]) > 80:\n GazeboMaze.close()\n agent.save_model('./models/')\n f = open(record_dir + '/DQN_nav' + str(maze_id) + '.txt', 'a+')\n for i in episode_rewards:\n f.write(str(i))\n f.write('\\n')\n f.close()\n print(\"Training End!\")\n break\n" }, { "path": "rl_nav/scripts/E2E_PPO.py", "content": "from tensorforce.agents import PPOAgent\nimport os\nimport env\nimport config\n\n# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\nmaze_id = config.maze_id\nrestore = False\n\nGazeboMaze = env.GazeboMaze(maze_id=maze_id, continuous=True)\n\nrecord_dir = 'record'\nif not os.path.exists(record_dir):\n os.makedirs(record_dir)\n\nsaver_dir = './models/nav{}'.format(maze_id)\nif not os.path.exists(saver_dir):\n os.makedirs(saver_dir)\n\nsummarizer_dir = './record/E2E_PPO/nav{}'.format(maze_id)\nif not os.path.exists(summarizer_dir):\n os.makedirs(summarizer_dir)\n\nstates = dict(\n image=GazeboMaze.states,\n previous_act=dict(shape=(2,), type='float'),\n relative_pos=dict(shape=(2,), type='float')\n)\n\nnetwork_spec = [\n [\n dict(type='input', names=['image']),\n dict(type='conv2d', size=32, window=(8, 6), stride=4, activation='relu', padding='SAME'),\n dict(type='conv2d', size=64, window=(4, 3), stride=2, activation='relu', padding='SAME'),\n dict(type='pool2d', pooling_type='max', window=2, stride=2, padding='SAME'),\n dict(type='conv2d', size=64, window=2, stride=2, activation='relu', padding='SAME'),\n dict(type='flatten'),\n dict(type='output', name='image_output')\n ],\n\n\n [\n dict(type='input', names=['image_output', 'previous_act', 'relative_pos'], aggregation_type='concat'),\n dict(type='dense', size=512, activation='relu'),\n dict(type='dense', size=512, activation='relu'),\n\n ]\n\n]\n\nmemory = dict(\n type='replay',\n include_next_states=True,\n capacity=10000\n)\n\nexploration = dict(\n type='epsilon_decay',\n initial_epsilon=1.0,\n final_epsilon=0.1,\n timesteps=100000,\n start_timestep=0\n)\n\noptimizer = dict(\n type='adam',\n learning_rate=0.0001\n)\n\n# Instantiate a Tensorforce agent\nagent = PPOAgent(\n states=states,\n actions=GazeboMaze.actions,\n network=network_spec,\n # update_mode=update_model,\n # memory=memory,\n # actions_exploration=exploration,\n saver=dict(directory=saver_dir, basename='E2E_PPO_model.ckpt', load=restore, seconds=6000),\n summarizer=dict(directory=summarizer_dir, labels=[\"graph\", \"losses\", \"reward\", \"entropy\"], seconds=6000),\n step_optimizer=optimizer,\n)\n\n\nepisode = 0\nepisode_rewards = []\nsuccesses = []\ntotal_timestep = 0\nmax_timesteps = 1000\nmax_episodes = 50000\n\n\nwhile True:\n observation = GazeboMaze.reset()\n observation = observation / 255.0 # normalize\n agent.reset()\n\n timestep = 0\n episode_reward = 0\n success = False\n\n while True:\n previous_act = GazeboMaze.vel_cmd\n print(previous_act)\n relative_pos = GazeboMaze.p\n state = dict(image=observation, previous_act=previous_act, relative_pos=relative_pos)\n # state = dict(image=observation, previous_act=GazeboMaze.vel_cmd, relative_pos=GazeboMaze.p)\n\n # Query the agent for its action decision\n action = agent.act(state)\n # Execute the decision and retrieve the current information\n observation, terminal, reward = GazeboMaze.execute(action)\n observation = observation / 255.0 # normalize\n # print(reward)\n # Pass feedback about performance (and termination) to the agent\n agent.observe(terminal=terminal, reward=reward)\n timestep += 1\n episode_reward += reward\n if terminal or timestep == max_timesteps:\n success = GazeboMaze.success\n break\n\n episode += 1\n total_timestep += timestep\n # avg_reward = float(episode_reward)/timestep\n successes.append(success)\n episode_rewards.append([episode_reward, timestep, success])\n\n # if total_timestep > 100000:\n # print('{}th episode reward: {}'.format(episode, episode_reward))\n\n if episode % 100 == 0:\n f = open(record_dir + '/E2E_PPO_nav' + str(maze_id) + '.txt', 'a+')\n for i in episode_rewards:\n f.write(str(i))\n f.write('\\n')\n f.close()\n episode_rewards = []\n agent.save_model('./models/')\n\n if len(successes) > 100:\n if sum(successes[-100:]) > 80:\n GazeboMaze.close()\n agent.save_model('./models/')\n f = open(record_dir + '/E2E_PPO_nav' + str(maze_id) + '.txt', 'a+')\n for i in episode_rewards:\n f.write(str(i))\n f.write('\\n')\n f.close()\n print(\"Training End!\")\n break\n\n # if episode == max_episodes:\n # break\n\n" }, { "path": "rl_nav/scripts/E2E_PPO_rnn.py", "content": "from tensorforce.agents import PPOAgent\nimport os\nimport env\nimport config\n\n# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\nmaze_id = config.maze_id\nrestore = False\n\nGazeboMaze = env.GazeboMaze(maze_id=maze_id, continuous=True)\n\nrecord_dir = 'record'\nif not os.path.exists(record_dir):\n os.makedirs(record_dir)\n\nsaver_dir = './models/nav{}'.format(maze_id)\nif not os.path.exists(saver_dir):\n os.makedirs(saver_dir)\n\nsummarizer_dir = './record/E2E_PPO_rnn/nav{}'.format(maze_id)\nif not os.path.exists(summarizer_dir):\n os.makedirs(summarizer_dir)\n\nstates = dict(\n image=GazeboMaze.states,\n relative_pos=dict(shape=(2,), type='float'),\n previous_act=dict(shape=(2,), type='float'),\n previous_reward=dict(shape=(1,), type='float')\n)\n\n\nnetwork_spec = [\n [\n dict(type='input', names=['image']),\n dict(type='conv2d', size=32, window=(8, 6), stride=4, activation='relu', padding='SAME'),\n dict(type='conv2d', size=64, window=(4, 3), stride=2, activation='relu', padding='SAME'),\n dict(type='pool2d', pooling_type='max', window=2, stride=2, padding='SAME'),\n dict(type='conv2d', size=64, window=2, stride=2, activation='relu', padding='SAME'),\n dict(type='flatten'),\n dict(type='dense', size=32, activation='relu'),\n dict(type='output', name='image_output')\n ],\n [\n dict(type='input', names=['image_output', 'relative_pos'], aggregation_type='concat'),\n dict(type='internal_lstm', size=256),\n dict(type='output', name='lstm_output'),\n ],\n\n [\n dict(type='input', names=['lstm_output', 'previous_act', 'previous_reward'], aggregation_type='concat'),\n dict(type='internal_lstm', size=256),\n dict(type='dense', size=256, activation='relu'),\n ]\n\n]\n\nmemory = dict(\n type='replay',\n include_next_states=True,\n capacity=10000\n)\n\nexploration = dict(\n type='epsilon_decay',\n initial_epsilon=1.0,\n final_epsilon=0.1,\n timesteps=100000,\n start_timestep=0\n)\n\noptimizer = dict(\n type='adam',\n learning_rate=0.0001\n)\n\n# Instantiate a Tensorforce agent\nagent = PPOAgent(\n states=states,\n actions=GazeboMaze.actions,\n network=network_spec,\n # update_mode=update_model,\n # memory=memory,\n # actions_exploration=exploration,\n saver=dict(directory=saver_dir, basename='E2E_PPO_rnn_model.ckpt', load=restore, seconds=6000),\n summarizer=dict(directory=summarizer_dir, labels=[\"graph\", \"losses\", \"reward\", \"entropy\"], seconds=6000),\n step_optimizer=optimizer,\n)\n\n\nepisode = 0\ntest_episode = 0\ntotal_timestep = 0\nmax_timesteps = 1000\nmax_episodes = 10000\nepisode_rewards = []\ntest_rewards = []\nsuccesses = []\ntest_successes = []\n\nwhile True:\n agent.reset()\n observation = GazeboMaze.reset()\n observation = observation / 255.0 # normalize\n\n timestep = 0\n episode_reward = 0\n success = False\n\n if GazeboMaze.goal not in config.test_space[maze_id]: # train\n while True:\n relative_pos = GazeboMaze.p\n previous_act = GazeboMaze.vel_cmd\n previous_reward = GazeboMaze.reward\n print(previous_act)\n state = dict(image=observation, relative_pos=relative_pos,\n previous_act=previous_act, previous_reward=[previous_reward])\n # state = dict(image=observation, previous_act=GazeboMaze.vel_cmd, relative_pos=GazeboMaze.p)\n\n # Query the agent for its action decision\n action = agent.act(state)\n # Execute the decision and retrieve the current information\n observation, terminal, reward = GazeboMaze.execute(action)\n observation = observation / 255.0 # normalize\n # print(reward)\n # Pass feedback about performance (and termination) to the agent\n agent.observe(terminal=terminal, reward=reward)\n timestep += 1\n episode_reward += reward\n if terminal or timestep == max_timesteps:\n success = GazeboMaze.success\n break\n\n episode += 1\n total_timestep += timestep\n # avg_reward = float(episode_reward)/timestep\n episode_rewards.append([episode_reward, timestep, success])\n\n # if total_timestep > 100000:\n # print('{}th episode reward: {}'.format(episode, episode_reward))\n\n if episode % 1000 == 0:\n f = open(record_dir + '/E2E_PPO_rnn_nav{}'.format(maze_id) + '.txt', 'a+')\n for i in episode_rewards:\n f.write(str(i))\n f.write('\\n')\n f.close()\n episode_rewards = []\n agent.save_model('./models/')\n\n else: # test\n while True:\n relative_pos = GazeboMaze.p\n previous_act = GazeboMaze.vel_cmd\n previous_reward = GazeboMaze.reward\n print(previous_act)\n state = dict(image=observation, relative_pos=relative_pos,\n previous_act=previous_act, previous_reward=[previous_reward])\n # state = dict(image=observation, previous_act=GazeboMaze.vel_cmd, relative_pos=GazeboMaze.p)\n\n # Query the agent for its action decision\n action = agent.act(state, independent=True)\n # Execute the decision and retrieve the current information\n observation, terminal, reward = GazeboMaze.execute(action)\n observation = observation / 255.0 # normalize\n # print(reward)\n # Pass feedback about performance (and termination) to the agent\n # agent.observe(terminal=terminal, reward=reward)\n timestep += 1\n episode_reward += reward\n if terminal or timestep == max_timesteps:\n success = GazeboMaze.success\n break\n\n test_episode += 1\n test_successes.append(success)\n test_rewards.append([episode_reward, timestep, success])\n\n if test_episode % 100 == 0:\n f = open(record_dir + '/E2E_PPO_rnn_test_nav{}'.format(maze_id) + '.txt', 'a+')\n for i in test_rewards:\n f.write(str(i))\n f.write('\\n')\n f.close()\n test_rewards = []\n\n if len(test_successes) > 100:\n if sum(test_successes[-100:]) > 60:\n GazeboMaze.close()\n agent.save_model('./models/')\n # save test data\n f = open(record_dir + '/E2E_PPO_rnn_test_nav{}'.format(maze_id) + '.txt', 'a+')\n for i in test_rewards:\n f.write(str(i))\n f.write('\\n')\n f.close()\n # save training data\n f = open(record_dir + '/E2E_PPO_rnn_nav{}'.format(maze_id) + '.txt', 'a+')\n for i in episode_rewards:\n f.write(str(i))\n f.write('\\n')\n f.close()\n print(\"Training End!\")\n break\n" }, { "path": "rl_nav/scripts/PPO.py", "content": "from tensorforce.agents import PPOAgent\n\nimport os\nimport itertools\n\nimport config\nimport env\nimport worldModels.VAE\n\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n\nmaze_id = config.maze_id\nrestore = False\n\nGazeboMaze = env.GazeboMaze(maze_id=maze_id, continuous=True)\n\nrecord_dir = 'record'\nif not os.path.exists(record_dir):\n os.makedirs(record_dir)\n\nsaver_dir = './models/nav{}'.format(maze_id)\nif not os.path.exists(saver_dir):\n os.makedirs(saver_dir)\n\nsummarizer_dir = './record/PPO/nav{}'.format(maze_id)\nif not os.path.exists(summarizer_dir):\n os.makedirs(summarizer_dir)\n\nvae = worldModels.VAE.VAE()\nvae.set_weights(config.vae_weight)\n\n\n# Network as list of layers\n# reference: https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Training-PPO.md\nnetwork_spec = [\n dict(type='dense', size=512, activation='relu'), # 'none'\n # dict(type='tf_layer', layer='batch_normalization', center=True, scale=True),\n # dict(type=\"nonlinearity\", name='relu'),\n dict(type='dense', size=512, activation='relu'),\n # dict(type='tf_layer', layer='batch_normalization', center=True, scale=True),\n # dict(type=\"nonlinearity\", name='relu'),\n dict(type='dense', size=512, activation='relu'),\n # dict(type='tf_layer', layer='batch_normalization', center=True, scale=True),\n # dict(type=\"nonlinearity\", name='relu')\n]\n\n\nmemory = dict(\n type='replay',\n include_next_states=False,\n capacity=10000\n)\n\nexploration = dict(\n type='epsilon_decay',\n initial_epsilon=0.1,\n final_epsilon=0.01,\n timesteps=100000,\n start_timestep=0\n)\n\n\noptimizer = dict(\n type='adam',\n learning_rate=0.0001\n)\n\n# Instantiate a Tensorforce agent\nagent = PPOAgent(\n states=dict(shape=(36,), type='float'), # GazeboMaze.states,\n actions=GazeboMaze.actions,\n network=network_spec,\n # memory=memory,\n # actions_exploration=exploration,\n saver=dict(directory=saver_dir, basename='PPO_model.ckpt', load=restore, seconds=10800),\n summarizer=dict(directory=summarizer_dir, labels=[\"graph\", \"losses\", \"reward\", \"entropy\"], seconds=10800),\n step_optimizer=optimizer\n)\n\n\nepisode = 0\ntotal_timestep = 0\nmax_timesteps = 1000\nmax_episodes = 10000\nepisode_rewards = []\nsuccesses = []\n\nwhile True:\n agent.reset()\n observation = GazeboMaze.reset()\n observation = observation / 255.0 # normalize\n\n timestep = 0\n episode_reward = 0\n success = False\n\n while True:\n latent_vector = vae.get_vector(observation.reshape(1, 48, 64, 3))\n latent_vector = list(itertools.chain(*latent_vector)) # [[ ]] -> [ ]\n relative_pos = GazeboMaze.p\n previous_act = GazeboMaze.vel_cmd\n print(previous_act)\n state = latent_vector + relative_pos + previous_act\n # print(state)\n\n # Query the agent for its action decision\n action = agent.act(state)\n # Execute the decision and retrieve the current information\n observation, terminal, reward = GazeboMaze.execute(action)\n observation = observation / 255.0 # normalize\n # print(reward)\n # Pass feedback about performance (and termination) to the agent\n agent.observe(terminal=terminal, reward=reward)\n timestep += 1\n episode_reward += reward\n if terminal or timestep == max_timesteps:\n success = GazeboMaze.success\n break\n\n episode += 1\n total_timestep += timestep\n # avg_reward = float(episode_reward)/timestep\n successes.append(success)\n episode_rewards.append([episode_reward, timestep, success])\n\n # if total_timestep > 100000:\n # print('{}th episode reward: {}'.format(episode, episode_reward))\n\n if episode % 100 == 0:\n f = open(record_dir + '/PPO_nav' + str(maze_id) + '.txt', 'a+')\n for i in episode_rewards:\n f.write(str(i))\n f.write('\\n')\n f.close()\n episode_rewards = []\n agent.save_model('./models/')\n\n if len(successes) > 100:\n if sum(successes[-100:]) > 80:\n GazeboMaze.close()\n agent.save_model('./models/')\n f = open(record_dir + '/PPO_nav' + str(maze_id) + '.txt', 'a+')\n for i in episode_rewards:\n f.write(str(i))\n f.write('\\n')\n f.close()\n print(\"Training End!\")\n break\n\n # if episode == max_episodes:\n # break\n\n" }, { "path": "rl_nav/scripts/PPO_remap.py", "content": "from tensorforce.agents import PPOAgent\n\nimport os\nimport itertools\n\nimport config\nimport env\nimport worldModels.VAE\n\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n\nmaze_id = config.maze_id\ndeterministic = config.deterministic\nrestore = False\n\nGazeboMaze = env.GazeboMaze(maze_id=maze_id, continuous=True)\n\nrecord_dir = 'record'\nif not os.path.exists(record_dir):\n os.makedirs(record_dir)\n\nsaver_dir = './models/nav{}'.format(maze_id)\nif not os.path.exists(saver_dir):\n os.makedirs(saver_dir)\n\nsummarizer_dir = './record/PPO_remap/nav{}'.format(maze_id)\nif not os.path.exists(summarizer_dir):\n os.makedirs(summarizer_dir)\n\nvae = worldModels.VAE.VAE()\nvae.set_weights(config.vae_weight)\n\nstates = dict(\n latent_vector=dict(shape=(32,), type='float'),\n previous_act=dict(shape=(2,), type='float'),\n relative_pos=dict(shape=(2,), type='float')\n)\n\nnetwork_spec = [\n [\n dict(type='input', names=['latent_vector']),\n dict(type='dense', size=32, activation='tanh'),\n dict(type='output', name='feature_remap')\n ],\n\n [\n dict(type='input', names=['feature_remap', 'previous_act', 'relative_pos'], aggregation_type='concat'),\n dict(type='dense', size=512, activation='relu'),\n dict(type='dense', size=512, activation='relu'),\n dict(type='dense', size=512, activation='relu'),\n\n ]\n\n]\n\n\nmemory = dict(\n type='replay',\n include_next_states=False,\n capacity=10000\n)\n\nexploration = dict(\n type='epsilon_decay',\n initial_epsilon=1.0,\n final_epsilon=0.1,\n timesteps=100000,\n start_timestep=0\n)\n\n\noptimizer = dict(\n type='adam',\n learning_rate=0.0001\n)\n\n\n# Instantiate a Tensorforce agent\nagent = PPOAgent(\n states=states, # GazeboMaze.states,\n actions=GazeboMaze.actions,\n network=network_spec,\n # memory=memory,\n # actions_exploration=exploration,\n saver=dict(directory=saver_dir, basename='PPO_remap_model.ckpt', load=restore, seconds=10800),\n summarizer=dict(directory=summarizer_dir, labels=[\"graph\", \"losses\", \"reward\", \"entropy\"], seconds=10800),\n step_optimizer=optimizer\n)\n\n\nepisode = 0\ntotal_timestep = 0\nmax_timesteps = 1000\nmax_episodes = 10000\nepisode_rewards = []\nsuccesses = []\n\nwhile True:\n agent.reset()\n observation = GazeboMaze.reset()\n observation = observation / 255.0 # normalize\n\n timestep = 0\n episode_reward = 0\n success = False\n\n while True:\n latent_vector = vae.get_vector(observation.reshape(1, 48, 64, 3))\n latent_vector = list(itertools.chain(*latent_vector)) # [[ ]] -> [ ]\n relative_pos = GazeboMaze.p\n previous_act = GazeboMaze.vel_cmd\n print(previous_act)\n # state = latent_vector + relative_pos + previous_act\n state = dict(latent_vector=latent_vector, previous_act=previous_act, relative_pos=relative_pos)\n # print(state)\n\n # Query the agent for its action decision\n action = agent.act(state, deterministic=deterministic)\n # Execute the decision and retrieve the current information\n observation, terminal, reward = GazeboMaze.execute(action)\n observation = observation / 255.0 # normalize\n # print(reward)\n # Pass feedback about performance (and termination) to the agent\n agent.observe(terminal=terminal, reward=reward)\n timestep += 1\n episode_reward += reward\n if terminal or timestep == max_timesteps:\n success = GazeboMaze.success\n break\n\n episode += 1\n total_timestep += timestep\n # avg_reward = float(episode_reward)/timestep\n successes.append(success)\n episode_rewards.append([episode_reward, timestep, success])\n\n # if total_timestep > 100000:\n # print('{}th episode reward: {}'.format(episode, episode_reward))\n\n if episode % 1000 == 0:\n f = open(record_dir + '/PPO_remap_episode' + str(episode) + '.txt', 'a+')\n for i in episode_rewards:\n f.write(str(i))\n f.write('\\n')\n f.close()\n episode_rewards = []\n agent.save_model('./models/')\n\n if len(successes) > 100:\n if sum(successes[-100:]) > 50:\n GazeboMaze.close()\n agent.save_model('./models/')\n break\n\n # if episode == max_episodes:\n # break\n" }, { "path": "rl_nav/scripts/PPO_rnn.py", "content": "from tensorforce.agents import PPOAgent\n\nimport os\nimport itertools\n\nimport config\nimport env\nimport worldModels.VAE\n\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n\nmaze_id = config.maze_id\ndeterministic = config.deterministic\nrestore = False\n\nGazeboMaze = env.GazeboMaze(maze_id=maze_id, continuous=True)\n\nrecord_dir = 'record'\nif not os.path.exists(record_dir):\n os.makedirs(record_dir)\n\nsaver_dir = './models/nav{}'.format(maze_id)\nif not os.path.exists(saver_dir):\n os.makedirs(saver_dir)\n\nsummarizer_dir = './record/PPO_rnn/nav{}'.format(maze_id)\nif not os.path.exists(summarizer_dir):\n os.makedirs(summarizer_dir)\n\nvae = worldModels.VAE.VAE()\nvae.set_weights(config.vae_weight)\n\n\n# Network as list of layers\n# reference: https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Training-PPO.md\nnetwork_spec = [\n [\n dict(type='input', names=['latent_vector', 'relative_pos'], aggregation_type='concat'),\n dict(type='internal_lstm', size=256),\n dict(type='output', name='lstm_output'),\n ],\n [\n dict(type='input', names=['lstm_output', 'previous_act', 'previous_reward'], aggregation_type='concat'),\n dict(type='internal_lstm', size=256),\n dict(type='dense', size=256, activation='relu')\n ]\n]\n\n\nmemory = dict(\n type='replay',\n include_next_states=False,\n capacity=10000\n)\n\nexploration = dict(\n type='epsilon_decay',\n initial_epsilon=1.0,\n final_epsilon=0.1,\n timesteps=1000000,\n start_timestep=0\n)\n\n\noptimizer = dict(\n type='adam',\n learning_rate=0.0001\n)\n\n\nstates = dict(\n latent_vector=dict(shape=(32,), type='float'),\n relative_pos=dict(shape=(2,), type='float'),\n previous_act=dict(shape=(2,), type='float'),\n previous_reward=dict(shape=(1,), type='float')\n)\n\n# Instantiate a Tensorforce agent\nagent = PPOAgent(\n states=states, # dict(shape=(37,), type='float'), # GazeboMaze.states,\n actions=GazeboMaze.actions,\n network=network_spec,\n # memory=memory,\n # actions_exploration=exploration,\n saver=dict(directory=saver_dir, basename='PPO_rnn_nav{}_model.ckpt'.format(maze_id), load=restore, seconds=6000),\n summarizer=dict(directory=summarizer_dir, labels=[\"graph\", \"losses\", \"reward\", \"entropy\"], seconds=6000),\n step_optimizer=optimizer\n)\n\n\nepisode = 0\ntest_episode = 0\ntotal_timestep = 0\nmax_timesteps = 1000\nmax_episodes = 10000\nepisode_rewards = []\ntest_rewards = []\nsuccesses = []\ntest_successes = []\n\nwhile True:\n agent.reset()\n observation = GazeboMaze.reset()\n observation = observation / 255.0 # normalize\n\n timestep = 0\n episode_reward = 0\n success = False\n\n if GazeboMaze.goal not in config.test_space[maze_id]: # train\n while True:\n latent_vector = vae.get_vector(observation.reshape(1, 48, 64, 3))\n latent_vector = list(itertools.chain(*latent_vector)) # [[ ]] -> [ ]\n relative_pos = GazeboMaze.p\n previous_act = GazeboMaze.vel_cmd\n previous_reward = GazeboMaze.reward\n print(previous_act)\n # state = latent_vector + relative_pos + previous_act + [previous_reward]\n state = dict(latent_vector=latent_vector, relative_pos=relative_pos,\n previous_act=previous_act, previous_reward=[previous_reward])\n # print(state)\n\n # Query the agent for its action decision\n action = agent.act(state, deterministic=deterministic)\n # Execute the decision and retrieve the current information\n observation, terminal, reward = GazeboMaze.execute(action)\n observation = observation / 255.0 # normalize\n # print(reward)\n # Pass feedback about performance (and termination) to the agent\n agent.observe(terminal=terminal, reward=reward)\n timestep += 1\n episode_reward += reward\n if terminal or timestep == max_timesteps:\n success = GazeboMaze.success\n break\n\n episode += 1\n total_timestep += timestep\n # avg_reward = float(episode_reward)/timestep\n successes.append(success)\n episode_rewards.append([episode_reward, timestep, success])\n\n # if total_timestep > 100000:\n # print('{}th episode reward: {}'.format(episode, episode_reward))\n\n if episode % 1000 == 0:\n f = open(record_dir + '/PPO_rnn_nav{}'.format(maze_id) + '.txt', 'a+')\n for i in episode_rewards:\n f.write(str(i))\n f.write('\\n')\n f.close()\n episode_rewards = []\n agent.save_model('./models/')\n\n else: # test\n while True:\n latent_vector = vae.get_vector(observation.reshape(1, 48, 64, 3))\n latent_vector = list(itertools.chain(*latent_vector)) # [[ ]] -> [ ]\n relative_pos = GazeboMaze.p\n previous_act = GazeboMaze.vel_cmd\n previous_reward = GazeboMaze.reward\n print(previous_act)\n # state = latent_vector + relative_pos + previous_act + [previous_reward]\n state = dict(latent_vector=latent_vector, relative_pos=relative_pos,\n previous_act=previous_act, previous_reward=[previous_reward])\n # print(state)\n\n # Query the agent for its action decision\n action = agent.act(state, independent=True)\n # Execute the decision and retrieve the current information\n observation, terminal, reward = GazeboMaze.execute(action)\n observation = observation / 255.0 # normalize\n # print(reward)\n # Pass feedback about performance (and termination) to the agent\n # agent.observe(terminal=terminal, reward=reward)\n timestep += 1\n episode_reward += reward\n if terminal or timestep == max_timesteps:\n success = GazeboMaze.success\n break\n\n # avg_reward = float(episode_reward)/timestep\n test_episode += 1\n test_successes.append(success)\n test_rewards.append([episode_reward, timestep, success])\n\n # if total_timestep > 100000:\n # print('{}th episode reward: {}'.format(episode, episode_reward))\n\n if test_episode % 100 == 0:\n f = open(record_dir + '/PPO_rnn_test_nav{}'.format(maze_id) + '.txt', 'a+')\n for i in test_rewards:\n f.write(str(i))\n f.write('\\n')\n f.close()\n test_rewards = []\n\n if len(test_successes) > 100:\n if sum(test_successes[-100:]) > 60:\n GazeboMaze.close()\n agent.save_model('./models/')\n # save test data\n f = open(record_dir + '/PPO_rnn_test_nav{}'.format(maze_id) + '.txt', 'a+')\n for i in test_rewards:\n f.write(str(i))\n f.write('\\n')\n f.close()\n # save training data\n f = open(record_dir + '/PPO_rnn_nav{}'.format(maze_id) + '.txt', 'a+')\n for i in episode_rewards:\n f.write(str(i))\n f.write('\\n')\n f.close()\n print(\"Training End!\")\n break\n\n # if episode == max_episodes:\n # break\n" }, { "path": "rl_nav/scripts/PPO_rnn_remap.py", "content": "from tensorforce.agents import PPOAgent\n\nimport os\nimport itertools\n\nimport config\nimport env\nimport worldModels.VAE\n\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n\nmaze_id = config.maze_id\ndeterministic = config.deterministic\nrestore = False\n\nGazeboMaze = env.GazeboMaze(maze_id=maze_id, continuous=True)\n\nrecord_dir = 'record'\nif not os.path.exists(record_dir):\n os.makedirs(record_dir)\n\nsaver_dir = './models/nav{}'.format(maze_id)\nif not os.path.exists(saver_dir):\n os.makedirs(saver_dir)\n\nsummarizer_dir = './record/PPO_rnn_remap/nav{}'.format(maze_id)\nif not os.path.exists(summarizer_dir):\n os.makedirs(summarizer_dir)\n\nvae = worldModels.VAE.VAE()\nvae.set_weights(config.vae_weight)\n\n\n# Network as list of layers\n# reference: https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Training-PPO.md\nnetwork_spec = [\n [\n dict(type='input', names=['latent_vector']),\n dict(type='dense', size=32, activation='tanh'),\n dict(type='output', name='feature_remap')\n ],\n [\n dict(type='input', names=['feature_remap', 'relative_pos'], aggregation_type='concat'),\n dict(type='internal_lstm', size=256),\n dict(type='output', name='lstm_output'),\n ],\n [\n dict(type='input', names=['lstm_output', 'previous_act', 'previous_reward'], aggregation_type='concat'),\n dict(type='internal_lstm', size=256),\n dict(type='dense', size=256, activation='relu')\n ]\n]\n\n\nmemory = dict(\n type='replay',\n include_next_states=False,\n capacity=10000\n)\n\nexploration = dict(\n type='epsilon_decay',\n initial_epsilon=1.0,\n final_epsilon=0.1,\n timesteps=1000000,\n start_timestep=0\n)\n\n\noptimizer = dict(\n type='adam',\n learning_rate=0.0001\n)\n\n\nstates = dict(\n latent_vector=dict(shape=(32,), type='float'),\n relative_pos=dict(shape=(2,), type='float'),\n previous_act=dict(shape=(2,), type='float'),\n previous_reward=dict(shape=(1,), type='float')\n)\n\n# Instantiate a Tensorforce agent\nagent = PPOAgent(\n states=states, # dict(shape=(37,), type='float'), # GazeboMaze.states,\n actions=GazeboMaze.actions,\n network=network_spec,\n # memory=memory,\n # actions_exploration=exploration,\n saver=dict(directory=saver_dir, basename='PPO_rnn__remap_nav{}_model.ckpt'.format(maze_id), load=restore, seconds=6000),\n summarizer=dict(directory=summarizer_dir, labels=[\"graph\", \"losses\", \"reward\", \"entropy\"], seconds=6000),\n step_optimizer=optimizer\n)\n\n\nepisode = 0\ntest_episode = 0\ntotal_timestep = 0\nmax_timesteps = 1000\nmax_episodes = 10000\nepisode_rewards = []\ntest_rewards = []\nsuccesses = []\ntest_successes = []\n\nwhile True:\n agent.reset()\n observation = GazeboMaze.reset()\n observation = observation / 255.0 # normalize\n\n timestep = 0\n episode_reward = 0\n success = False\n\n if GazeboMaze.goal not in config.test_space[maze_id]: # train\n while True:\n latent_vector = vae.get_vector(observation.reshape(1, 48, 64, 3))\n latent_vector = list(itertools.chain(*latent_vector)) # [[ ]] -> [ ]\n relative_pos = GazeboMaze.p\n previous_act = GazeboMaze.vel_cmd\n previous_reward = GazeboMaze.reward\n print(previous_act)\n # state = latent_vector + relative_pos + previous_act + [previous_reward]\n state = dict(latent_vector=latent_vector, relative_pos=relative_pos,\n previous_act=previous_act, previous_reward=[previous_reward])\n # print(state)\n\n # Query the agent for its action decision\n action = agent.act(state, deterministic=deterministic)\n # Execute the decision and retrieve the current information\n observation, terminal, reward = GazeboMaze.execute(action)\n observation = observation / 255.0 # normalize\n # print(reward)\n # Pass feedback about performance (and termination) to the agent\n agent.observe(terminal=terminal, reward=reward)\n timestep += 1\n episode_reward += reward\n if terminal or timestep == max_timesteps:\n success = GazeboMaze.success\n break\n\n episode += 1\n total_timestep += timestep\n # avg_reward = float(episode_reward)/timestep\n successes.append(success)\n episode_rewards.append([episode_reward, timestep, success])\n\n # if total_timestep > 100000:\n # print('{}th episode reward: {}'.format(episode, episode_reward))\n\n if episode % 1000 == 0:\n f = open(record_dir + '/PPO_rnn_remap_nav{}'.format(maze_id) + '.txt', 'a+')\n for i in episode_rewards:\n f.write(str(i))\n f.write('\\n')\n f.close()\n episode_rewards = []\n agent.save_model('./models/')\n\n else: # test\n while True:\n latent_vector = vae.get_vector(observation.reshape(1, 48, 64, 3))\n latent_vector = list(itertools.chain(*latent_vector)) # [[ ]] -> [ ]\n relative_pos = GazeboMaze.p\n previous_act = GazeboMaze.vel_cmd\n previous_reward = GazeboMaze.reward\n print(previous_act)\n # state = latent_vector + relative_pos + previous_act + [previous_reward]\n state = dict(latent_vector=latent_vector, relative_pos=relative_pos,\n previous_act=previous_act, previous_reward=[previous_reward])\n # print(state)\n\n # Query the agent for its action decision\n action = agent.act(state, independent=True)\n # Execute the decision and retrieve the current information\n observation, terminal, reward = GazeboMaze.execute(action)\n observation = observation / 255.0 # normalize\n # print(reward)\n # Pass feedback about performance (and termination) to the agent\n # agent.observe(terminal=terminal, reward=reward)\n timestep += 1\n episode_reward += reward\n if terminal or timestep == max_timesteps:\n success = GazeboMaze.success\n break\n\n # avg_reward = float(episode_reward)/timestep\n test_episode += 1\n test_successes.append(success)\n test_rewards.append([episode_reward, timestep, success])\n\n # if total_timestep > 100000:\n # print('{}th episode reward: {}'.format(episode, episode_reward))\n\n if test_episode % 100 == 0:\n f = open(record_dir + '/PPO_rnn_remap_test_nav{}'.format(maze_id) + '.txt', 'a+')\n for i in test_rewards:\n f.write(str(i))\n f.write('\\n')\n f.close()\n test_rewards = []\n\n if len(test_successes) > 100:\n if sum(test_successes[-100:]) > 50:\n GazeboMaze.close()\n agent.save_model('./models/')\n break\n\n # if episode == max_episodes:\n # break\n" }, { "path": "rl_nav/scripts/config.py", "content": "import rospkg\npath = rospkg.RosPack().get_path('rl_nav')\n\n# Environment related\nmaze_id = 1\ndeterministic = False\ncontinuous = True # if the action space is continuous. True for PPO, DDPG and False for DQN\n# image\ninput_dim = (48, 64, 3)\n# reward parameter\nr_arrive = 100\nr_collision = -50 # -100\nCr = 100\nCp = -0.05 # time step penalty\n# goal\ngoal_space = []\nstart_space = []\ntest_space = []\ngoal_space_nav0 = [[-2.8, -4.0]]\ntest_space_nav0 = [goal_space_nav0[-1]]\ngoal_space_nav1 = [[-3.8, -3.8], [-1.5, -4], [-3, -2.2]]\ntest_space_nav1 = [goal_space_nav1[-1]]\ngoal_space_nav2 = [[-1.8, -3.2], [-2, -2.6], [-3, -3.2], [-0.8, -3.5], [-0.4, -2.6], [-2.5, -3], [-3, -3], [-2, -1.9],\n [-1.3, -3.8], [-3.3, -4], [-3, -3.8], [-1.2, -3.2]]\ntest_space_nav2 = [goal_space_nav2[-1], goal_space_nav2[-2]]\nstart_space_nav0 = [[1., -3.], [1., -4.], [0, -4.], [0, -1.8], [-1, -3], [-1.8, -1.9], [-1.7, -3.8]]\nstart_space_nav1 = [[-0.4, -2.6]] # ,4.0/3*math.pi\nstart_space_nav2 = [[0.65, -3.0]] # ,1.0/2*math.pi\nstart_space_nav3 = [[-3.8, -3.5], [-3.8, -4.1], [0, -3], [-1, -4.1], [-2, -4.1], [-3, -4.1], [-3, -2.5], [-1.2, -3.2],\n [-1.2, -2.5], [-2, -3.2], [0, -2.5], [0, -3.5], [0, -4], [-2.5, -4.1], [-2.5, -3.2], [-3, -2.5],\n [-3.5, -2.8], [-3.5, -2.5]]\ngoal_space.append(goal_space_nav0)\ngoal_space.append(goal_space_nav1)\ngoal_space.append(goal_space_nav2)\ntest_space.append(test_space_nav0)\ntest_space.append(test_space_nav1)\ntest_space.append(test_space_nav2)\n\nstart_space.append(start_space_nav0)\nstart_space.append(start_space_nav1)\nstart_space.append(start_space_nav2)\nstart_space.append(start_space_nav3)\nCd = 0.354/2 # = r when testing\n# max linear velocity\nv_max = 0.3 # 0.5 # m/s\n# max angular velocity\nw_max = 1.0 # 1.2 # rad/s\n\n# VAE related\nlatent_vector_dim = 32\nvae_weight = path + '/scripts/worldModels/models/vae_weights.h5'\n\n# RNN related\ndir_name = 'maze1_rnn_data' # which dir to save the rnn data\nrnn_weight = path + '/scripts/worldModels/models/rnn_weights.h5'\nepisode = 2 # < 1000, for testing RNN\nframe = 25 # < 50, for testing RNN\n" }, { "path": "rl_nav/scripts/env.py", "content": "from __future__ import absolute_import\nfrom __future__ import print_function\n\nimport rospy\nimport tf\nimport roslaunch\nimport rospkg\nfrom geometry_msgs.msg import Twist\nfrom std_srvs.srv import Empty\nfrom sensor_msgs.msg import Image\n# from sensor_msgs.msg import LaserScan\nfrom gazebo_msgs.msg import ContactsState\nfrom gazebo_msgs.msg import ModelState\nfrom gazebo_msgs.srv import GetModelState\nfrom gazebo_msgs.srv import SetModelState\n\nfrom tensorforce.environments import Environment\nimport numpy as np\nimport math\nimport time\nimport cv2\nfrom cv_bridge import CvBridge, CvBridgeError\nimport config\n# reward parameter\nr_arrive = config.r_arrive\nr_collision = config.r_collision\nCr = config.Cr # judge arrival\nCd = config.Cd # compute reward if no collision and arrival\nCp = config.Cp # time step penalty\nv_max = config.v_max # max linear velocity\nw_max = config.w_max # max angular velocity\ncontinuous = config.continuous\n\nclass GazeboMaze(Environment):\n \"\"\"\n Base environment class.\n \"\"\"\n def __init__(self, maze_id=0, continuous=continuous):\n self.maze_id = maze_id\n self.continuous = continuous\n self.goal_space = config.goal_space[maze_id]\n self.start_space = config.start_space[maze_id]\n # Launch the simulation with the given launch file name\n '''\n uuid = roslaunch.rlutil.get_or_generate_uuid(None, False)\n cli_args = ['rl_nav', 'nav_gazebo.launch'] # , 'maze_id:={}'.format(self.maze_id)]\n roslaunch_args = cli_args[2:]\n roslaunch_file = [(roslaunch.rlutil.resolve_launch_arguments(cli_args), roslaunch_args)]\n launch = roslaunch.parent.ROSLaunchParent(uuid, roslaunch_file)\n launch.start() \n '''\n uuid = roslaunch.rlutil.get_or_generate_uuid(None, False)\n roslaunch.configure_logging(uuid)\n path = rospkg.RosPack().get_path('rl_nav') # 'rospack find' python api, find the path of rl_nav package\n self.launch = roslaunch.parent.ROSLaunchParent(uuid, [path + '/launch/nav_gazebo.launch'])\n self.launch.start()\n\n rospy.init_node('env_node')\n time.sleep(10) # Wait for gzserver to launch\n\n self.vel_pub = rospy.Publisher('/mobile_base/commands/velocity', Twist, queue_size=5)\n self.unpause = rospy.ServiceProxy('/gazebo/unpause_physics', Empty)\n self.pause = rospy.ServiceProxy('/gazebo/pause_physics', Empty)\n self.reset_proxy = rospy.ServiceProxy('/gazebo/reset_simulation', Empty)\n self.get_state = rospy.ServiceProxy('/gazebo/get_model_state', GetModelState)\n self.set_state = rospy.ServiceProxy('/gazebo/set_model_state', SetModelState)\n\n '''\n self.goal = self.goal_space[np.random.choice(len(self.goal_space))]\n start = self.start_space[np.random.choice(len(self.start_space))]\n self.set_start(start[0], start[1], np.random.uniform(0, 2*math.pi))\n d0, theta0 = self.rectangular2polar(self.goal[0]-start[0], self.goal[1]-start[1])\n self.p = [d0, theta0] # relative target position \n '''\n self.goal = []\n self.p = []\n self.reward = 0\n self.success = False\n\n self.img_height = config.input_dim[0]\n self.img_width = config.input_dim[1]\n self.img_channels = config.input_dim[2]\n self._states = dict(shape=(self.img_height, self.img_width, self.img_channels), type='float')\n self._actions = dict(num_actions=5, type='int')\n if self.continuous:\n self._actions = dict(linear_vel=dict(shape=(), type='float', min_value=0.0, max_value=1.0),\n angular_vel=dict(shape=(), type='float', min_value=-1.0, max_value=1.0))\n self.vel_cmd = [0., 0.]\n\n def __str__(self):\n raise 'GazeMaze ({})'.format(self.maze_id)\n\n def close(self):\n \"\"\"\n Close environment. No other method calls possible afterwards.\n \"\"\"\n self.launch.shutdown()\n time.sleep(10)\n\n def seed(self, seed):\n \"\"\"\n Sets the random seed of the environment to the given value (current time, if seed=None).\n Naturally deterministic Environments (e.g. ALE or some gym Envs) don't have to implement this method.\n\n Args:\n seed (int): The seed to use for initializing the pseudo-random number generator (default=epoch time in sec).\n Returns: The actual seed (int) used OR None if Environment did not override this method (no seeding supported).\n \"\"\"\n return None\n\n def reset(self):\n \"\"\"\n Reset environment and setup for new episode.\n\n Returns:\n initial state of reset environment.\n \"\"\"\n\n # Resets the state of the environment and returns an initial observation.\n start_index = np.random.choice(len(self.start_space))\n goal_index = np.random.choice(len(self.goal_space))\n # start_index, goal_index = np.random.choice(len(self.start_space), 2, replace=False)\n start = self.start_space[start_index]\n theta = np.random.uniform(0, 2.0*math.pi) # 1.0/2*math.pi # 4.0/3*math.pi # \n self.set_start(start[0], start[1], theta)\n self.goal = self.goal_space[goal_index]\n self.set_goal(self.goal[0], self.goal[1])\n d0, alpha0 = self.goal2robot(self.goal[0] - start[0], self.goal[1] - start[1], theta)\n # print(d0, alpha0)\n self.p = [d0, alpha0] # relative target position\n self.reward = 0\n\n self.success = False\n self.vel_cmd = [0., 0.]\n\n rospy.wait_for_service('/gazebo/reset_simulation')\n try:\n # reset_proxy.call()\n self.reset_proxy\n except rospy.ServiceException:\n print(\"/gazebo/reset_simulation service call failed\")\n\n # Unpause simulation to make observation\n rospy.wait_for_service('/gazebo/unpause_physics')\n try:\n # self.unpause\n rospy.ServiceProxy('/gazebo/unpause_physics', Empty)\n except rospy.ServiceException:\n print(\"/gazebo/unpause_physics service call failed\")\n\n image_data = None\n cv_image = None\n while image_data is None:\n image_data = rospy.wait_for_message('/camera/rgb/image_raw', Image)\n # h = image_data.height\n # w = image_data.width\n cv_image = CvBridge().imgmsg_to_cv2(image_data, \"bgr8\")\n\n rospy.wait_for_service('/gazebo/pause_physics')\n try:\n self.pause\n except rospy.ServiceException:\n print(\"/gazebo/pause_physics service call failed\")\n\n if self.img_channels == 1:\n cv_image = cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY)\n cv_image = cv2.resize(cv_image, (self.img_width, self.img_height)) # width, height\n\n observation = cv_image # .reshape(1, 1, cv_image.shape[0], cv_image.shape[1])\n return observation\n\n def execute(self, action):\n \"\"\"\n Executes action, observes next state(s) and reward.\n\n Args:\n action: Actions to execute.\n\n Returns:\n Tuple of (next state, bool indicating terminal, reward)\n \"\"\"\n rospy.wait_for_service('/gazebo/unpause_physics')\n try:\n self.unpause\n except rospy.ServiceException:\n print(\"/gazebo/unpause_physics service call failed\")\n\n vel_cmd = Twist()\n if self.continuous:\n vel_cmd.linear.x = v_max*action['linear_vel']\n vel_cmd.angular.z = w_max*action['angular_vel']\n else:\n # 3 actions\n if action == 0: # Left\n vel_cmd.linear.x = 0.25\n vel_cmd.angular.z = 1.0\n elif action == 1: # H-LEFT\n vel_cmd.linear.x = 0.25\n vel_cmd.angular.z = 0.4\n elif action == 2: # Straight\n vel_cmd.linear.x = 0.25\n vel_cmd.angular.z = 0\n elif action == 3: # H-Right\n vel_cmd.linear.x = 0.25\n vel_cmd.angular.z = -0.4\n elif action == 4: # Right\n vel_cmd.linear.x = 0.25\n vel_cmd.angular.z = -1.0\n else:\n raise Exception('Error discrete action: {}'.format(action))\n\n self.vel_cmd = [vel_cmd.linear.x, vel_cmd.angular.z]\n self.vel_pub.publish(vel_cmd)\n time.sleep(0.05)\n\n done = False\n self.reward = 0\n image_data = None\n cv_image = None\n\n while image_data is None:\n image_data = rospy.wait_for_message('/camera/rgb/image_raw', Image, timeout=5)\n cv_image = CvBridge().imgmsg_to_cv2(image_data, \"bgr8\")\n\n if self.img_channels == 1:\n cv_image = cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY)\n cv_image = cv2.resize(cv_image, (self.img_width, self.img_height))\n observation = cv_image\n\n contact_data = None\n while contact_data is None:\n contact_data = rospy.wait_for_message('/contact_state', ContactsState, timeout=5)\n collision = contact_data.states != []\n if collision:\n done = True\n self.reward = r_collision\n print(\"collision!\")\n # print(collision, contact_data.states)\n\n robot_state = None\n rospy.wait_for_service('/gazebo/get_model_state')\n try:\n get_state = self.get_state # create a handle for calling the service\n # use the handle just like a normal function, \"robot\" relative to \"world\"\n robot_state = get_state(\"robot\", \"world\")\n assert robot_state.success is True\n except rospy.ServiceException:\n print(\"/gazebo/get_model_state service call failed\")\n\n position = robot_state.pose.position\n orientation = robot_state.pose.orientation\n d_x = self.goal[0] - position.x\n d_y = self.goal[1] - position.y\n\n _, _, theta = tf.transformations.euler_from_quaternion(\n [orientation.x, orientation.y, orientation.z, orientation.w])\n d, alpha = self.goal2robot(d_x, d_y, theta)\n # print(d, alpha)\n if d < Cd:\n done = True\n self.reward = r_arrive\n self.success = True\n print(\"arrival!\")\n\n if not done:\n delta_d = self.p[0] - d\n self.reward = Cr*delta_d + Cp\n\n rospy.wait_for_service('/gazebo/pause_physics')\n try:\n # resp_pause = pause.call()\n self.pause\n except rospy.ServiceException:\n print(\"/gazebo/pause_physics service call failed\")\n\n self.p = [d, alpha]\n\n return observation, done, self.reward\n\n @property\n def states(self):\n \"\"\"\n Return the state space. Might include subdicts if multiple states are\n available simultaneously.\n\n Returns:\n States specification, with the following attributes\n (required):\n - type: one of 'bool', 'int', 'float' (default: 'float').\n - shape: integer, or list/tuple of integers (required).\n \"\"\"\n return self._states\n\n @property\n def actions(self):\n \"\"\"\n Return the action space. Might include subdicts if multiple actions are\n available simultaneously.\n\n Returns:\n actions (spec, or dict of specs): Actions specification, with the following attributes\n (required):\n - type: one of 'bool', 'int', 'float' (required).\n - shape: integer, or list/tuple of integers (default: []).\n - num_actions: integer (required if type == 'int').\n - min_value and max_value: float (optional if type == 'float', default: none).\n \"\"\"\n return self._actions\n\n def goal2robot(self, d_x, d_y, theta):\n d = math.sqrt(d_x * d_x + d_y * d_y)\n alpha = math.atan2(d_y, d_x) - theta\n return d, alpha\n\n def set_start(self, x, y, theta):\n state = ModelState()\n state.model_name = 'robot'\n state.reference_frame = 'world' # ''ground_plane'\n # pose\n state.pose.position.x = x\n state.pose.position.y = y\n state.pose.position.z = 0\n quaternion = tf.transformations.quaternion_from_euler(0, 0, theta)\n state.pose.orientation.x = quaternion[0]\n state.pose.orientation.y = quaternion[1]\n state.pose.orientation.z = quaternion[2]\n state.pose.orientation.w = quaternion[3]\n # twist\n state.twist.linear.x = 0\n state.twist.linear.y = 0\n state.twist.linear.z = 0\n state.twist.angular.x = 0\n state.twist.angular.y = 0\n state.twist.angular.z = 0\n\n rospy.wait_for_service('/gazebo/set_model_state')\n try:\n set_state = self.set_state\n result = set_state(state)\n assert result.success is True\n except rospy.ServiceException:\n print(\"/gazebo/get_model_state service call failed\")\n\n def set_goal(self, x, y):\n state = ModelState()\n state.model_name = 'goal'\n state.reference_frame = 'world' # ''ground_plane'\n state.pose.position.x = x\n state.pose.position.y = y\n state.pose.position.z = 0.1\n\n rospy.wait_for_service('/gazebo/set_model_state')\n try:\n set_state = self.set_state\n result = set_state(state)\n assert result.success is True\n except rospy.ServiceException:\n print(\"/gazebo/get_model_state service call failed\")\n" }, { "path": "rl_nav/scripts/test.py", "content": "import rospy\nimport tf\nimport roslaunch\nfrom geometry_msgs.msg import Twist\nfrom std_srvs.srv import Empty\nfrom sensor_msgs.msg import Image\n# from sensor_msgs.msg import LaserScan\nfrom gazebo_msgs.msg import ContactsState\nfrom gazebo_msgs.msg import ModelState\nfrom gazebo_msgs.srv import GetModelState\nfrom gazebo_msgs.srv import SetModelState\n\nfrom tensorforce.environments import Environment\nimport numpy as np\nimport math\nimport cv2\nfrom cv_bridge import CvBridge, CvBridgeError\n\nimport config\nimport time\nimport env\nfrom std_msgs.msg import Float32\n\n\nuuid = roslaunch.rlutil.get_or_generate_uuid(None, False)\nroslaunch.configure_logging(uuid)\nlaunch = roslaunch.parent.ROSLaunchParent(uuid, ['/home/maroon/catkin_ws/src/rl_nav/launch/nav_gazebo.launch'])\nlaunch.start()\nrospy.init_node('test')\ntime.sleep(10)\n\n\ndef set_start(x, y, theta):\n state = ModelState()\n state.model_name = 'robot'\n state.reference_frame = 'world' # ''ground_plane'\n # pose\n state.pose.position.x = x\n state.pose.position.y = y\n state.pose.position.z = 0\n quaternion = tf.transformations.quaternion_from_euler(0, 0, theta)\n state.pose.orientation.x = quaternion[0]\n state.pose.orientation.y = quaternion[1]\n state.pose.orientation.z = quaternion[2]\n state.pose.orientation.w = quaternion[3]\n # twist\n state.twist.linear.x = 0\n state.twist.linear.y = 0\n state.twist.linear.z = 0\n state.twist.angular.x = 0\n state.twist.angular.y = 0\n state.twist.angular.z = 0\n\n rospy.wait_for_service('/gazebo/set_model_state')\n try:\n set_state = rospy.ServiceProxy('/gazebo/set_model_state', SetModelState)\n result = set_state(state)\n assert result.success is True\n print(\"set the model state successfully\")\n except rospy.ServiceException:\n print(\"/gazebo/get_model_state service call failed\")\n\n\nstart = [0, 0]\nset_start(start[0], start[1], math.pi)\n\nprint('reset simulation ...')\nrospy.wait_for_service('/gazebo/reset_simulation')\ntry:\n # reset_proxy.call()\n rospy.ServiceProxy('/gazebo/reset_simulation', Empty)\nexcept rospy.ServiceException:\n print(\"/gazebo/reset_simulation service call failed\")\nprint('reset simulation successfully')\n\n\nrospy.wait_for_service('/gazebo/pause_physics')\ntry:\n # resp_pause = pause.call()\n rospy.ServiceProxy('/gazebo/pause_physics', Empty)\n print(\"pause physics successfully\")\nexcept rospy.ServiceException:\n print(\"/gazebo/pause_physics service call failed\")\n\ntime.sleep(5)\n'''\nrospy.wait_for_service('/gazebo/unpause_physics')\ntry:\n # resp_pause = pause.call()\n rospy.ServiceProxy('/gazebo/unpause_physics', Empty)\n print(\"unpause physics successfully\")\nexcept rospy.ServiceException:\n print(\"/gazebo/unpause_physics service call failed\")\n'''\n\n\nget_state = rospy.ServiceProxy('/gazebo/get_model_state', GetModelState)\nrobot_state = None\nrospy.wait_for_service('/gazebo/get_model_state')\ntry:\n get_state = get_state\n robot_state = get_state(\"robot\", \"world\") # \"robot\" relative to \"world\"\n assert robot_state.success is True\nexcept rospy.ServiceException:\n print(\"/gazebo/get_model_state service call failed\")\npos = robot_state.pose\nprint(pos)\n\n\nprint('capture image ...')\nimage_data = None\ncv_image = None\nwhile image_data is None:\n image_data = rospy.wait_for_message('/camera/rgb/image_raw', Image)\n # h = image_data.height\n # w = image_data.width\n cv_image = CvBridge().imgmsg_to_cv2(image_data, \"bgr8\")\n print(np.asarray(cv_image).reshape([-1, 64, 48, 3]))\nprint('capture image successfully')\n\nprint('get contact state')\ncontact_data = None\nwhile contact_data is None:\n contact_data = rospy.wait_for_message('/contact_state', ContactsState, 5.0)\ncollision = contact_data.states != []\nprint(collision)\n\nlaunch.shutdown()\n\n'''\n# Unpause simulation to make observation\nprint('unpause physics ...')\nrospy.wait_for_service('/gazebo/unpause_physics')\ntry:\n # resp_pause = pause.call()\n # self.unpause()\n rospy.ServiceProxy('/gazebo/unpause_physics', Empty)\nexcept rospy.ServiceException:\n print(\"/gazebo/unpause_physics service call failed\")\nprint('unpause physics successfully')\n\nprint('get contact state')\ncontact_data = None\nwhile contact_data is None:\n contact_data = rospy.wait_for_message('/contact_state', ContactsState, 5.0)\ncollision = contact_data.states != []\nprint(collision)\n\nprint('capture image ...')\nimage_data = None\ncv_image = None\nwhile image_data is None:\n image_data = rospy.wait_for_message('/camera/rgb/image_raw', Image)\n # h = image_data.height\n # w = image_data.width\n cv_image = CvBridge().imgmsg_to_cv2(image_data, \"bgr8\")\nprint('capture image successfully')\n\n\nprint('pause physics ...')\nrospy.wait_for_service('/gazebo/pause_physics')\ntry:\n # resp_pause = pause.call()\n pause = rospy.ServiceProxy('/gazebo/pause_physics', Empty)\nexcept rospy.ServiceException:\n print(\"/gazebo/pause_physics service call failed\")\nprint('pause physics successfully')\n\n'''\n\n\n\n\n" }, { "path": "rl_nav/scripts/worldModels/01_generate_data.py", "content": "import os\nimport sys\nimport argparse\nimport numpy as np\n\nsys.path.append(\"..\")\nimport env\n\ndir_name = 'record'\nif not os.path.exists(dir_name):\n os.makedirs(dir_name)\n\n\ndef main(args):\n maze_id = args.maze_id\n GazeboMaze = env.GazeboMaze(maze_id=maze_id, continuous=True)\n print(\"Generating data for env maze{}\".format(maze_id))\n file_number = args.file_number\n total_episodes = args.total_episodes\n # random_generated_int = np.random.randint(0, 2 ** 31 - 1)\n # np.random.seed(random_generated_int)\n time_steps = args.time_steps\n for i in range(file_number):\n total_frames = 0\n obs_data = []\n action_data = []\n episode = 0\n while episode < total_episodes:\n print('---------------------------')\n obs_sequence = []\n action_sequence = []\n\n observation = GazeboMaze.reset()\n done = False\n t = 0\n\n while t < time_steps:\n action = dict()\n action['linear_vel'] = np.random.uniform(0, 1)\n action['angular_vel'] = np.random.uniform(-1, 1)\n # print(action)\n obs_sequence.append(observation)\n action_sequence.append([action['linear_vel'], action['angular_vel']])\n t += 1\n\n observation, done, reward = GazeboMaze.execute(action)\n\n total_frames += t\n episode += 1\n obs_data.append(obs_sequence)\n action_data.append(action_sequence)\n print(\"{}th collections\".format(i))\n print(\"Episode {} finished after {} timesteps\".format(episode, t))\n print(\"Current dataset contains {} observations\".format(total_frames))\n\n print(\"Saving dataset ...\")\n np.save(dir_name+'/observation'+str(i), obs_data)\n np.save(dir_name+'/action'+str(i), action_data)\n GazeboMaze.close()\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='Generate new training data')\n parser.add_argument('--maze_id', type=int, default=1, help='which maze ')\n parser.add_argument('--file_number', type=int, default=300, help='total number of files to generate')\n parser.add_argument('--total_episodes', type=int, default=3000, help='how many episodes you need to record in a file')\n parser.add_argument('--time_steps', type=int, default=10, help='how many timesteps in an episode?')\n\n args = parser.parse_args()\n main(args)\n" }, { "path": "rl_nav/scripts/worldModels/02_train_vae.py", "content": "# python 02_train_vae.py --new_model\n\nimport argparse\nimport numpy as np\nimport VAE\n\nimport sys\nsys.path.append(\"..\")\nimport config\n\n\ndef main(args):\n new_model = args.new_model\n\n vae = VAE.VAE()\n\n if not new_model:\n try:\n vae.set_weights(config.vae_weight)\n except:\n print(\"Either set --new_model or ensure\" + config.vae_weight + \" exists\")\n raise\n\n for j in range(2):\n for i in range(130):\n data = np.load('./record/observation{}.npy'.format(i), encoding='latin1')\n data = np.array([item for episode in data for item in episode])\n # np.random.seed(0)\n # indices = np.random.choice(data.shape[0], 40000, replace=False)\n data = data.astype(np.float) / 255.0\n vae.train(data, j, i)\n\n vae.plot_loss()\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description=('Train VAE'))\n parser.add_argument('--new_model', type=bool, default=False, help='start a new model from scratch?')\n args = parser.parse_args()\n\n main(args)\n" }, { "path": "rl_nav/scripts/worldModels/RNN.py", "content": "import math\n\nfrom keras.layers import Input, LSTM, Dense\nfrom keras.models import Model\nfrom keras import backend as K\nfrom keras.callbacks import EarlyStopping, Callback\nfrom keras.optimizers import Adam, SGD\nimport matplotlib.pyplot as plt\nimport sys\n\nimport numpy as np\nsys.path.append(\"..\")\nimport config\n\nZ_DIM = config.latent_vector_dim\nACTION_DIM = 2\n\nHIDDEN_UNITS = 256\n\nBATCH_SIZE = 32\nEPOCHS = 5\n\n\n# draw the training loss\nclass LossHistory(Callback):\n def __init__(self):\n self.losses = {'batch': [], 'epoch': []}\n self.accuracy = {'batch': [], 'epoch': []}\n self.val_loss = {'batch': [], 'epoch': []}\n self.val_acc = {'batch': [], 'epoch': []}\n\n def on_train_begin(self, logs={}):\n pass\n\n def on_batch_end(self, batch, logs={}):\n self.losses['batch'].append(logs.get('loss'))\n self.accuracy['batch'].append(logs.get('acc'))\n self.val_loss['batch'].append(logs.get('val_loss'))\n self.val_acc['batch'].append(logs.get('val_acc'))\n\n def on_epoch_end(self, batch, logs={}):\n self.losses['epoch'].append(logs.get('loss'))\n self.accuracy['epoch'].append(logs.get('acc'))\n self.val_loss['epoch'].append(logs.get('val_loss'))\n self.val_acc['epoch'].append(logs.get('val_acc'))\n\n def loss_plot(self, loss_type):\n iters = range(len(self.losses[loss_type]))\n plt.figure()\n # acc\n # plt.plot(iters, self.accuracy[loss_type], 'r', label='train acc')\n # loss\n plt.plot(iters, self.losses[loss_type], 'g', label='train loss')\n if loss_type == 'epoch':\n # val_acc\n # plt.plot(iters, self.val_acc[loss_type], 'b', label='val acc')\n # val_loss\n plt.plot(iters, self.val_loss[loss_type], 'k', label='val loss')\n plt.grid(True)\n plt.xlabel(loss_type)\n plt.ylabel('loss')\n plt.legend(loc=\"upper right\")\n plt.show()\n\n\nclass RNN():\n def __init__(self):\n self.models = self._build()\n self.model = self.models[0]\n self.forward = self.models[1]\n self.history = LossHistory()\n self.z_dim = Z_DIM\n self.action_dim = ACTION_DIM\n self.hidden_units = HIDDEN_UNITS\n\n def _build(self):\n # the model that will be trained\n rnn_x = Input(shape=(None, Z_DIM + ACTION_DIM))\n lstm = LSTM(HIDDEN_UNITS, return_sequences=True, return_state=True)\n\n lstm_output, _, _ = lstm(rnn_x)\n mdn = Dense(Z_DIM)(lstm_output)\n\n rnn = Model(rnn_x, mdn)\n\n # the model used during prediction\n state_input_h = Input(shape=(HIDDEN_UNITS,))\n state_input_c = Input(shape=(HIDDEN_UNITS,))\n state_inputs = [state_input_h, state_input_c]\n \n _, state_h, state_c = lstm(rnn_x, initial_state=state_inputs)\n forward = Model([rnn_x] + state_inputs, [state_h, state_c])\n\n optimizer = Adam(lr=0.0001)\n # optimizer = SGD(lr=0.0001, decay=1e-4, momentum=0.9, nesterov=True)\n rnn.compile(loss='mean_squared_error', optimizer=optimizer)\n\n return [rnn, forward]\n\n def set_weights(self, filepath):\n self.model.load_weights(filepath)\n\n def train(self, rnn_input, rnn_output,):\n callbacks_list = [self.history]\n\n self.model.fit(rnn_input, rnn_output,\n shuffle=True,\n epochs=EPOCHS,\n batch_size=BATCH_SIZE,\n callbacks=callbacks_list)\n\n self.model.save_weights('./models/rnn_weights_RNN.h5')\n\n def plot_loss(self):\n self.history.loss_plot('batch')\n\n def save_weights(self, filepath):\n self.model.save_weights(filepath)\n\n # predict the output\n def get_output(self, input_data):\n output = self.model.predict(input_data)\n return output\n\n" }, { "path": "rl_nav/scripts/worldModels/VAE.py", "content": "import numpy as np\n\nfrom keras.layers import Input, Conv2D, Flatten, Dense, Conv2DTranspose, Lambda, Reshape\nfrom keras.models import Model\nfrom keras.regularizers import l2\nfrom keras import backend as K # https://keras.io/backend/\nfrom keras.optimizers import Adam, SGD\nfrom keras.callbacks import Callback\nimport matplotlib.pyplot as plt\n\n\nimport sys\nsys.path.append(\"..\")\nimport config\n\nINPUT_DIM = config.input_dim\n# Encoder\nCONV_FILTERS = [32, 64, 128, 256]\nCONV_KERNEL_SIZES = [(3, 4), (3, 4), (3, 4), (3, 4)]\nCONV_STRIDES = [2, 2, 2, 2]\nCONV_ACTIVATIONS = ['relu', 'relu', 'relu', 'relu']\n\nDENSE_SIZE = 1024\n\n# Decoder\nCONV_T_FILTERS = [128, 64, 32, 3]\nCONV_T_KERNEL_SIZES = [(4, 5), (4, 5), (4, 6), (6, 6)]\nCONV_T_STRIDES = [2, 2, 2, 2]\nCONV_T_ACTIVATIONS = ['relu', 'relu', 'relu', 'sigmoid']\n\nZ_DIM = config.latent_vector_dim\nEPOCHS = 2\nBATCH_SIZE = 256\n\n\ndef sampling(args):\n z_mean, z_log_var = args\n epsilon = K.random_normal(shape=(K.shape(z_mean)[0], Z_DIM), mean=0., stddev=1.)\n return z_mean + K.exp(z_log_var / 2) * epsilon\n\n\n# draw the training loss\nclass LossHistory(Callback):\n def __init__(self):\n self.losses = {'batch': [], 'epoch': []}\n self.accuracy = {'batch': [], 'epoch': []}\n self.val_loss = {'batch': [], 'epoch': []}\n self.val_acc = {'batch': [], 'epoch': []}\n\n def on_train_begin(self, logs={}):\n pass\n\n def on_batch_end(self, batch, logs={}):\n self.losses['batch'].append(logs.get('loss'))\n self.accuracy['batch'].append(logs.get('acc'))\n self.val_loss['batch'].append(logs.get('val_loss'))\n self.val_acc['batch'].append(logs.get('val_acc'))\n\n def on_epoch_end(self, batch, logs={}):\n self.losses['epoch'].append(logs.get('loss'))\n self.accuracy['epoch'].append(logs.get('acc'))\n self.val_loss['epoch'].append(logs.get('val_loss'))\n self.val_acc['epoch'].append(logs.get('val_acc'))\n\n def loss_plot(self, loss_type):\n iters = range(len(self.losses[loss_type]))\n plt.figure()\n # acc\n # plt.plot(iters, self.accuracy[loss_type], 'r', label='train acc')\n # loss\n plt.plot(iters, self.losses[loss_type], 'g', label='train loss')\n if loss_type == 'epoch':\n # val_acc\n # plt.plot(iters, self.val_acc[loss_type], 'b', label='val acc')\n # val_loss\n plt.plot(iters, self.val_loss[loss_type], 'k', label='val loss')\n plt.grid(True)\n plt.xlabel(loss_type)\n plt.ylabel('acc-loss')\n plt.legend(loc=\"upper right\")\n plt.show()\n\n\nclass VAE():\n def __init__(self):\n self.models = self._build()\n self.model = self.models[0]\n self.encoder = self.models[1]\n self.decoder = self.models[2]\n self.history = LossHistory()\n\n self.input_dim = INPUT_DIM\n self.z_dim = Z_DIM\n\n def _build(self):\n vae_x = Input(shape=INPUT_DIM)\n vae_c1 = Conv2D(filters=CONV_FILTERS[0], kernel_size=CONV_KERNEL_SIZES[0], strides=CONV_STRIDES[0],\n padding='valid', data_format='channels_last', activation=CONV_ACTIVATIONS[0])(vae_x) # , kernel_regularizer=l2(0.0001))(vae_x)\n vae_c2 = Conv2D(filters=CONV_FILTERS[1], kernel_size=CONV_KERNEL_SIZES[1], strides=CONV_STRIDES[1],\n padding='valid', data_format='channels_last', activation=CONV_ACTIVATIONS[1])(vae_c1) # , kernel_regularizer=l2(0.0001))(vae_c1)\n vae_c3 = Conv2D(filters=CONV_FILTERS[2], kernel_size=CONV_KERNEL_SIZES[2], strides=CONV_STRIDES[2],\n padding='valid', data_format='channels_last', activation=CONV_ACTIVATIONS[2])(vae_c2) # , kernel_regularizer=l2(0.0001))(vae_c2)\n vae_c4 = Conv2D(filters=CONV_FILTERS[3], kernel_size=CONV_KERNEL_SIZES[3], strides=CONV_STRIDES[3],\n padding='valid', data_format='channels_last', activation=CONV_ACTIVATIONS[3])(vae_c3) # , kernel_regularizer=l2(0.0001))(vae_c3)\n\n vae_z_in = Flatten()(vae_c4)\n\n vae_z_mean = Dense(Z_DIM)(vae_z_in) # , kernel_regularizer=l2(0.0001)\n vae_z_log_var = Dense(Z_DIM)(vae_z_in) # , kernel_regularizer=l2(0.0001)\n\n vae_z = Lambda(sampling)([vae_z_mean, vae_z_log_var])\n vae_z_input = Input(shape=(Z_DIM,))\n\n # we instantiate these layers separately so as to reuse them later\n vae_dense = Dense(1024) # , kernel_regularizer=l2(0.0001)\n vae_dense_model = vae_dense(vae_z)\n\n vae_z_out = Reshape((1, 1, DENSE_SIZE))\n vae_z_out_model = vae_z_out(vae_dense_model)\n\n vae_d1 = Conv2DTranspose(filters=CONV_T_FILTERS[0], kernel_size=CONV_T_KERNEL_SIZES[0], padding='valid',\n data_format='channels_last', strides=CONV_T_STRIDES[0], activation=CONV_T_ACTIVATIONS[0]) # , kernel_regularizer=l2(0.0001))\n vae_d1_model = vae_d1(vae_z_out_model)\n vae_d2 = Conv2DTranspose(filters=CONV_T_FILTERS[1], kernel_size=CONV_T_KERNEL_SIZES[1], padding='valid',\n data_format='channels_last', strides=CONV_T_STRIDES[1], activation=CONV_T_ACTIVATIONS[1]) # , kernel_regularizer=l2(0.0001))\n vae_d2_model = vae_d2(vae_d1_model)\n vae_d3 = Conv2DTranspose(filters=CONV_T_FILTERS[2], kernel_size=CONV_T_KERNEL_SIZES[2], padding='valid',\n data_format='channels_last', strides=CONV_T_STRIDES[2], activation=CONV_T_ACTIVATIONS[2]) # , kernel_regularizer=l2(0.0001))\n vae_d3_model = vae_d3(vae_d2_model)\n vae_d4 = Conv2DTranspose(filters=CONV_T_FILTERS[3], kernel_size=CONV_T_KERNEL_SIZES[3], padding='valid',\n data_format='channels_last', strides=CONV_T_STRIDES[3], activation=CONV_T_ACTIVATIONS[3]) # , kernel_regularizer=l2(0.0001))\n vae_d4_model = vae_d4(vae_d3_model)\n\n # DECODER ONLY\n vae_dense_decoder = vae_dense(vae_z_input)\n vae_z_out_decoder = vae_z_out(vae_dense_decoder)\n\n vae_d1_decoder = vae_d1(vae_z_out_decoder)\n vae_d2_decoder = vae_d2(vae_d1_decoder)\n vae_d3_decoder = vae_d3(vae_d2_decoder)\n vae_d4_decoder = vae_d4(vae_d3_decoder)\n\n # MODELS\n vae = Model(vae_x, vae_d4_model)\n vae_encoder = Model(vae_x, vae_z)\n vae_decoder = Model(vae_z_input, vae_d4_decoder)\n\n def vae_r_loss(y_true, y_pred):\n y_true_flat = K.flatten(y_true)\n y_pred_flat = K.flatten(y_pred)\n\n return 10 * K.mean(K.square(y_true_flat - y_pred_flat), axis=-1)\n\n def vae_kl_loss(y_true, y_pred):\n kl_loss = - 0.5 * K.mean(1 + vae_z_log_var - K.square(vae_z_mean) - K.exp(vae_z_log_var), axis=-1)\n # stop optimizing for KL loss term once it is lower than some level, rather than letting it go to near zero.\n # So optimize for tf.max(KL, good_enough_kl_level) instead, to relax the information bottleneck of the VAE.\n kl_tolerance = 0.5\n kl_loss = K.maximum(kl_loss, kl_tolerance*Z_DIM)\n return kl_loss\n\n def vae_loss(y_true, y_pred):\n return vae_r_loss(y_true, y_pred) + vae_kl_loss(y_true, y_pred)\n\n optimizer = Adam(lr=0.0001)\n # optimizer = SGD(lr=0.001, decay=1e-4, momentum=0.9, nesterov=True)\n vae.compile(optimizer=optimizer, loss=vae_loss, metrics=[vae_r_loss, vae_kl_loss])\n\n return [vae, vae_encoder, vae_decoder]\n\n def set_weights(self, filepath):\n self.model.load_weights(filepath)\n print(\"load weights {} successfully\".format(filepath))\n\n def train(self, data, j=0, i=0):\n callbacks_list = [self.history]\n\n self.model.fit(data, data,\n shuffle=True,\n epochs=EPOCHS,\n batch_size=BATCH_SIZE,\n callbacks=callbacks_list)\n\n self.model.save_weights('./models/vae_weights_' + str(j) + '_' + str(i) + '.h5')\n\n def plot_loss(self):\n self.history.loss_plot('batch')\n\n def save_weights(self, filepath):\n self.model.save_weights(filepath)\n\n def get_vector(self, obs_data):\n z = self.encoder.predict(np.array(obs_data))\n return z\n\n def get_output(self, input_data):\n input_data = np.asarray(input_data)\n if input_data.shape == (1, 32): # 32-dim vector\n output = self.decoder.predict(input_data)\n return output\n elif input_data.shape == (1, 48, 64, 3): # the raw image\n output = self.model.predict(input_data)\n return output\n else:\n raise Exception(\"Input shape {} is not right, it needs to be (1,32) or (1,48,64,3).\".format(input_data.shape))\n\n def generate_rnn_data(self, obs_data, action_data):\n rnn_input = []\n rnn_output = []\n\n for i, j in zip(obs_data, action_data):\n i = np.asarray(i) / 255.0\n rnn_z_input = self.encoder.predict(i)\n conc = [np.concatenate([x, y]) for x, y in zip(rnn_z_input, j)]\n rnn_input.append(conc[:-1])\n rnn_output.append(np.array(rnn_z_input[1:]))\n\n rnn_input = np.array(rnn_input)\n rnn_output = np.array(rnn_output)\n\n return [rnn_input, rnn_output]\n\n" }, { "path": "rl_nav/scripts/worldModels/VAE2.py", "content": "import numpy as np\n\nfrom keras.layers import Input, Conv2D, Flatten, Dense, Conv2DTranspose, Lambda, Reshape\nfrom keras.layers import BatchNormalization, Activation\nfrom keras.regularizers import l2\nfrom keras.optimizers import SGD\nfrom keras.optimizers import Adam\nfrom keras.models import Model\nfrom keras import backend as K\n\nimport sys\nsys.path.append(\"..\")\nimport config\n\nINPUT_DIM = config.input_dim\n# Encoder\nCONV_FILTERS = [32, 64, 128, 256]\nCONV_KERNEL_SIZES = [(3, 4), (3, 4), (3, 4), (3, 4)]\nCONV_STRIDES = [2, 2, 2, 2]\nCONV_ACTIVATIONS = ['relu', 'relu', 'relu', 'relu']\n\nDENSE_SIZE = 1024\n\n# Decoder\nCONV_T_FILTERS = [128, 64, 32, 3]\nCONV_T_KERNEL_SIZES = [(4, 5), (4, 5), (4, 6), (6, 6)]\nCONV_T_STRIDES = [2, 2, 2, 2]\nCONV_T_ACTIVATIONS = ['relu', 'relu', 'relu', 'sigmoid']\n\nZ_DIM = config.latent_vector_dim\nEPOCHS = 10\nBATCH_SIZE = 100\n\n\ndef sampling(args):\n z_mean, z_log_var = args\n epsilon = K.random_normal(shape=(K.shape(z_mean)[0], Z_DIM), mean=0., stddev=1.)\n return z_mean + K.exp(z_log_var / 2) * epsilon\n\n\nclass VAE():\n def __init__(self):\n self.models = self._build()\n self.model = self.models[0]\n self.encoder = self.models[1]\n self.decoder = self.models[2]\n\n self.input_dim = INPUT_DIM\n self.z_dim = Z_DIM\n\n def _build(self):\n vae_x = Input(shape=INPUT_DIM)\n vae_c1_conv = Conv2D(filters=CONV_FILTERS[0], kernel_size=CONV_KERNEL_SIZES[0], strides=CONV_STRIDES[0],\n padding='valid', data_format='channels_last', use_bias=False)(vae_x)\n vae_c1_bn = BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001)(vae_c1_conv)\n vae_c1 = Activation(CONV_ACTIVATIONS[0])(vae_c1_bn)\n\n vae_c2_conv = Conv2D(filters=CONV_FILTERS[1], kernel_size=CONV_KERNEL_SIZES[1], strides=CONV_STRIDES[1],\n padding='valid', data_format='channels_last', use_bias=False)(vae_c1)\n vae_c2_bn = BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001)(vae_c2_conv)\n vae_c2 = Activation(CONV_ACTIVATIONS[1])(vae_c2_bn)\n\n vae_c3_conv = Conv2D(filters=CONV_FILTERS[2], kernel_size=CONV_KERNEL_SIZES[2], strides=CONV_STRIDES[2],\n padding='valid', data_format='channels_last', use_bias=False)(vae_c2)\n vae_c3_bn = BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001)(vae_c3_conv)\n vae_c3 = Activation(CONV_ACTIVATIONS[2])(vae_c3_bn)\n\n vae_c4_conv = Conv2D(filters=CONV_FILTERS[3], kernel_size=CONV_KERNEL_SIZES[3], strides=CONV_STRIDES[3],\n padding='valid', data_format='channels_last', use_bias=False)(vae_c3)\n vae_c4_bn = BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001)(vae_c4_conv)\n vae_c4 = Activation(CONV_ACTIVATIONS[3])(vae_c4_bn)\n\n vae_z_in = Flatten()(vae_c4)\n\n vae_z_mean = Dense(Z_DIM)(vae_z_in)\n vae_z_log_var = Dense(Z_DIM)(vae_z_in)\n\n vae_z = Lambda(sampling)([vae_z_mean, vae_z_log_var])\n # vae_z_input = Input(shape=(Z_DIM,))\n\n # we instantiate these layers separately so as to reuse them later\n vae_dense = Dense(1024)\n vae_dense_model = vae_dense(vae_z)\n\n vae_z_out = Reshape((1, 1, DENSE_SIZE))(vae_dense_model)\n\n vae_d1_convT = Conv2DTranspose(filters=CONV_T_FILTERS[0], kernel_size=CONV_T_KERNEL_SIZES[0], padding='valid',\n data_format='channels_last', strides=CONV_T_STRIDES[0], use_bias=False)(vae_z_out)\n vae_d1_bn = BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001)(vae_d1_convT)\n vae_d1 = Activation(CONV_T_ACTIVATIONS[0])(vae_d1_bn)\n # vae_d1_model = vae_d1(vae_z_out_model)\n\n vae_d2_convT = Conv2DTranspose(filters=CONV_T_FILTERS[1], kernel_size=CONV_T_KERNEL_SIZES[1], padding='valid',\n data_format='channels_last', strides=CONV_T_STRIDES[1], use_bias=False)(vae_d1)\n vae_d2_bn = BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001)(vae_d2_convT)\n vae_d2 = Activation(CONV_T_ACTIVATIONS[1])(vae_d2_bn)\n # vae_d2_model = vae_d2(vae_d1_model)\n\n vae_d3_convT = Conv2DTranspose(filters=CONV_T_FILTERS[2], kernel_size=CONV_T_KERNEL_SIZES[2], padding='valid',\n data_format='channels_last', strides=CONV_T_STRIDES[2], use_bias=False)(vae_d2)\n vae_d3_bn = BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001)(vae_d3_convT)\n vae_d3 = Activation(CONV_T_ACTIVATIONS[2])(vae_d3_bn)\n # vae_d3_model = vae_d3(vae_d2_model)\n\n vae_d4_convT = Conv2DTranspose(filters=CONV_T_FILTERS[3], kernel_size=CONV_T_KERNEL_SIZES[3], padding='valid',\n data_format='channels_last', strides=CONV_T_STRIDES[3], use_bias=False)(vae_d3)\n vae_d4_bn = BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001)(vae_d4_convT)\n vae_d4 = Activation(CONV_T_ACTIVATIONS[3])(vae_d4_bn)\n # vae_d4_model = vae_d4(vae_d3_model)\n\n # MODELS\n vae = Model(vae_x, vae_d4)\n vae_encoder = Model(vae_x, vae_z)\n # vae_decoder = Model(vae_z_input, vae_d4)\n\n def vae_r_loss(y_true, y_pred):\n y_true_flat = K.flatten(y_true)\n y_pred_flat = K.flatten(y_pred)\n\n return 10 * K.mean(K.square(y_true_flat - y_pred_flat), axis=-1)\n\n def vae_kl_loss(y_true, y_pred):\n return - 0.5 * K.mean(1 + vae_z_log_var - K.square(vae_z_mean) - K.exp(vae_z_log_var), axis=-1)\n\n def vae_loss(y_true, y_pred):\n return vae_r_loss(y_true, y_pred) + vae_kl_loss(y_true, y_pred)\n\n # optimizer = SGD(lr=0.0001, decay=1e-4, momentum=0.9, nesterov=True)\n optimizer = Adam(lr=0.0001)\n vae.compile(optimizer=optimizer, loss=vae_loss, metrics=[vae_r_loss, vae_kl_loss])\n\n return (vae, vae_encoder, None) # vae_decoder)\n\n def set_weights(self, filepath):\n self.model.load_weights(filepath)\n\n def train(self, data, validation_split=0.0):\n self.model.fit(data, data,\n shuffle=True,\n epochs=EPOCHS,\n batch_size=BATCH_SIZE,\n validation_split=validation_split)\n\n self.model.save_weights(config.vae_weight)\n\n def save_weights(self, filepath):\n self.model.save_weights(filepath)\n\n def get_vector(self, obs_data):\n z = self.encoder.predict(np.array(obs_data))\n return z\n\n def get_output(self, obs_data):\n # output = self.decoder.predict(np.array(z))\n output = self.model.predict(obs_data)\n return output\n\n def generate_rnn_data(self, obs_data, action_data):\n rnn_input = []\n rnn_output = []\n\n for i, j in zip(obs_data, action_data):\n rnn_z_input = self.encoder.predict(np.array(i))\n conc = [np.concatenate([x, y]) for x, y in zip(rnn_z_input, j)]\n rnn_input.append(conc[:-1])\n rnn_output.append(np.array(rnn_z_input[1:]))\n\n rnn_input = np.array(rnn_input)\n rnn_output = np.array(rnn_output)\n\n return (rnn_input, rnn_output)\n" }, { "path": "rl_nav/scripts/worldModels/__init__.py", "content": "\n" }, { "path": "rl_nav/scripts/worldModels/test_rnn.py", "content": "import numpy as np\nfrom RNN import RNN\nfrom VAE import VAE\nfrom keras.preprocessing.sequence import pad_sequences\nimport matplotlib.pyplot as plt\nimport sys\n\nsys.path.append(\"..\")\nimport config\ndir_name = config.dir_name\nepisode = config.episode\nframe = config.frame\n\nnp.set_printoptions(precision=4, edgeitems=6, linewidth=100, suppress=True)\n\nvae = VAE()\nvae.set_weights(config.vae_weight)\n\nrnn = RNN()\nrnn.set_weights(config.rnn_weight)\n\n\nrnn_input = np.load(dir_name + '/rnn_input_0.npy')\nrnn_output = np.load(dir_name + '/rnn_output_0.npy')\nrnn_input = pad_sequences(rnn_input, maxlen=30, dtype='float32', padding='pre', truncating='pre')\nrnn_output = pad_sequences(rnn_output, maxlen=30, dtype='float32', padding='pre', truncating='pre')\n\n# print(rnn_input.shape)\n# print(rnn_output.shape)\ntrue = rnn_output[episode][frame]\nprint(\"The true value is: \")\nprint(true)\n\n# print(rnn_input[episode][frame])\npredict = rnn.get_output(np.expand_dims([rnn_input[episode][frame]], 0))\nprint(\"The prediction is: \")\nprint(predict)\n\nplt.subplot(1, 2, 1)\ntrue = true.reshape(1, 32)\ntrue = vae.get_output(true)\ntrue = true.reshape(48, 64, 3)[:, :, ::-1] # convert bgr to rgb format\nplt.imshow(true) # The rgb values should be in the range [0 .. 1] for floats or [0 .. 255] for integers.\nplt.title('Original')\n\nplt.subplot(1, 2, 2)\npredict = np.array(predict)\npredict = predict.reshape(1, 32)\npredict = vae.get_output(predict)\npredict = predict.reshape(48, 64, 3)[:, :, ::-1] # convert bgr to rgb format\nplt.imshow(predict) # The rgb values should be in the range [0 .. 1] for floats or [0 .. 255] for integers.\nplt.title('Prediction')\n\nplt.show()\n\n# for i in range(rnn_input[indices].shape[0]):\n# output = rnn.get_output(rnn_input[:i+1])\n# print(\"the real value is {}\".format(rnn_output[indices][i] - output))\n# # print(\"the predict value is {}\".format(output))\n" }, { "path": "rl_nav/scripts/worldModels/test_vae.py", "content": "import numpy as np\nimport VAE\nimport sys\nimport matplotlib.pyplot as plt\n\nsys.path.append(\"..\")\nimport config\n\nnp.set_printoptions(precision=4, edgeitems=6, linewidth=100, suppress=True)\n\nvae = VAE.VAE()\nvae.set_weights(config.vae_weight)\n\ndata = np.load('./record/observation3.npy')\ndata = np.array([item for episode in data for item in episode])\n\nindices = np.random.choice(data.shape[0])\nframe = data[indices] / 255.0\n# print(frame)\nplt.subplot(1, 2, 1)\ntest = frame[:, :, ::-1] # convert bgr to rgb format\nplt.imshow(test) # The rgb values should be in the range [0 .. 1] for floats or [0 .. 255] for integers.\nplt.title('Original')\n# plt.show()\n\nz = vae.get_vector(frame.reshape(1, 48, 64, 3))\nprint(z)\noutput = vae.get_output(z)\n# print(output)\nplt.subplot(1, 2, 2)\nresult = output.reshape(48, 64, 3)[:, :, ::-1]\nplt.imshow(result)\nplt.title('Output')\nplt.show()\n" }, { "path": "rl_nav/worlds/goal.urdf", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n \n 1\n Gazebo/Blue\n 0\n \n\n" }, { "path": "rl_nav/worlds/maze0.world", "content": "\n \n \n 1\n 0 0 10 0 -0 0\n 0.8 0.8 0.8 1\n 0.2 0.2 0.2 1\n \n 1000\n 0.9\n 0.01\n 0.001\n \n -0.5 0.1 -0.9\n \n \n 1\n \n \n \n \n 0 0 1\n 100 100\n \n \n \n \n \n 100\n 50\n \n \n \n \n \n \n \n \n \n \n 10\n \n \n 0\n \n \n 0 0 1\n 100 100\n \n \n \n \n \n \n 0\n 0\n 0\n \n \n 0 0 -9.8\n 6e-06 2.3e-05 -4.2e-05\n \n \n 0.001\n 1\n 1000\n \n \n 0.4 0.4 0.4 1\n 0.7 0.7 0.7 1\n 1\n \n \n \n EARTH_WGS84\n 0\n 0\n 0\n 0\n \n \n -0.655 -2.875 0 0 -0 0\n \n \n \n \n 3 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 3 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n -2.425 -0 0 0 -0 -1.5708\n 0\n 0\n 0\n \n \n \n \n \n 5 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 5 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 0 -1.425 0 0 -0 0\n 0\n 0\n 0\n \n \n \n \n \n 1.72926 0.3 1.5\n \n \n 0 0 0.75 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.75 0 -0 0\n \n \n 1.72926 0.3 1.5\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n -0.84263 0.525 0 0 -0 0\n 0\n 0\n 0\n \n \n \n \n \n 1.55 0.3 1.5\n \n \n 0.05 0 0.75 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0.05 0 0.75 0 -0 0\n \n \n 1.55 0.3 1.5\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n 0.747609 -0.037957 0 0 -0 1.5708\n 0\n 0\n 0\n \n \n \n \n \n 0.61 0.6 0.6\n \n \n 0 0 0.3 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.3 0 -0 0\n \n \n 0.61 0.6 0.6\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n -0.382696 -0.944783 0 0 -0 1.5708\n 0\n 0\n 0\n \n \n \n \n \n 0.61 0.6 0.6\n \n \n 0 0 0.3 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.3 0 -0 0\n \n \n 0.61 0.6 0.6\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n 1.69587 0.862287 0 0 -0 -1.5708\n 0\n 0\n 0\n \n \n \n \n \n 3 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 3 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 2.425 -0 0 0 -0 1.5708\n 0\n 0\n 0\n \n \n \n \n \n 5 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 5 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 0 1.425 0 0 -0 3.14159\n 0\n 0\n 0\n \n \n \n \n \n 1.5 0.3 1.5\n \n \n 0.08 0 0.75 0 -0 0 # 0 0 0.75 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0.08 0 0.75 0 -0 0\n \n \n 1.5 0.3 1.5\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n -1.54856 -0.158696 0 0 -0 1.5708\n 0\n 0\n 0\n \n 1\n \n \n 89 590000000\n 89 848295820\n 1547017414 421867553\n 89590\n \n -0.655 -2.875 0 0 -0 0\n 1 1 1\n \n -3.08 -2.875 0 0 0 -1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -0.655 -4.3 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -1.49763 -2.35 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n 0.092609 -2.91296 0 0 -0 1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -1.0377 -3.81978 0 0 -0 1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n 1.04087 -2.01271 0 0 0 -1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n 1.77 -2.875 0 0 -0 1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -0.655 -1.45 0 0 -0 3.14159\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -2.20356 -3.0337 0 0 -0 1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n \n 0 0 0 0 -0 0\n 1 1 1\n \n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n \n 0 0 10 0 -0 0\n \n \n \n \n -1.15113 -2.84816 8.89828 1.8e-05 1.5698 1.55629\n orbit\n perspective\n \n \n \n\n" }, { "path": "rl_nav/worlds/maze1.world", "content": "\n \n \n 1\n 0 0 10 0 -0 0\n 0.8 0.8 0.8 1\n 0.2 0.2 0.2 1\n \n 1000\n 0.9\n 0.01\n 0.001\n \n -0.5 0.1 -0.9\n \n \n 1\n \n \n \n \n 0 0 1\n 100 100\n \n \n \n \n \n 100\n 50\n \n \n \n \n \n \n \n \n \n \n 10\n \n \n 0\n \n \n 0 0 1\n 100 100\n \n \n \n \n \n \n 0\n 0\n \n \n 0 0 -9.8\n 6e-06 2.3e-05 -4.2e-05\n \n \n 0.01\n \n 1000 \n \n \n 0.4 0.4 0.4 1\n 0.7 0.7 0.7 1\n 1\n \n \n EARTH_WGS84\n 0\n 0\n 0\n 0\n \n \n -2.015 -3.115 0 0 -0 0\n \n \n \n \n 3 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 3 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n -2.425 0 0 0 0 -1.5708\n 0\n 0\n \n \n \n \n \n 5 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 5 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 0 -1.425 0 0 -0 0\n 0\n 0\n \n \n \n \n \n 1.15 0.3 1.5\n \n \n 0 0 0.75 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.75 0 -0 0\n \n \n 1.15 0.3 1.5\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n -0.597 -0.8375 0 0 0 -0.52367\n 0\n 0\n \n \n \n \n \n 3 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 3 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 2.425 0 0 0 -0 1.5708\n 0\n 0\n \n \n \n \n \n 5 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 5 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 0 1.425 0 0 -0 3.14159\n 0\n 0\n \n \n \n \n \n 0.61 0.6 0.6\n \n \n 0 0 0.3 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.3 0 -0 0\n \n \n 0.61 0.6 0.6\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n -1.765 0.61 0 0 0 -1.5708\n 0\n 0\n \n \n \n \n \n 0.61 0.6 0.6\n \n \n 0 0 0.3 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.3 0 -0 0\n \n \n 0.61 0.6 0.6\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n 1.435 -0.66 0 0 0 -1.5708\n 0\n 0\n \n \n \n \n \n 1.15 0.3 1.5\n \n \n 0 0 0.75 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.75 0 -0 0\n \n \n 1.15 0.3 1.5\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n 0.293 0.7925 0 0 0 -0.52367\n 0\n 0\n \n 1\n \n \n 418 803000000\n 420 724108\n 1551704375 806778605\n 418803\n \n -2.015 -3.115 0 0 -0 0\n 1 1 1\n \n -4.44 -3.115 0 0 0 -1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -2.015 -4.54 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -2.612 -3.9525 0 0 0 -0.52367\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n 0.41 -3.115 0 0 -0 1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -2.015 -1.69 0 0 -0 3.14159\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -3.78 -2.505 0 0 0 -1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -0.58 -3.775 0 0 0 -1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -1.722 -2.3225 0 0 0 -0.52367\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n \n 0 0 0 0 -0 0\n 1 1 1\n \n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n \n 0 0 10 0 -0 0\n \n \n \n \n 1.39137 -1.56664 39.8699 -2e-06 1.5498 1.60423\n orbit\n perspective\n \n \n \n\n" }, { "path": "rl_nav/worlds/maze2.world", "content": "\n \n \n 1\n 0 0 10 0 -0 0\n 0.8 0.8 0.8 1\n 0.2 0.2 0.2 1\n \n 1000\n 0.9\n 0.01\n 0.001\n \n -0.5 0.1 -0.9\n \n \n 1\n \n \n \n \n 0 0 1\n 100 100\n \n \n \n \n \n 100\n 50\n \n \n \n \n \n \n \n \n \n \n 10\n \n \n 0\n \n \n 0 0 1\n 100 100\n \n \n \n \n \n \n 0\n 0\n \n \n 0 0 -9.8\n 6e-06 2.3e-05 -4.2e-05\n \n \n 0.01\n 1000\n \n \n \n 0.4 0.4 0.4 1\n 0.7 0.7 0.7 1\n 1\n \n \n EARTH_WGS84\n 0\n 0\n 0\n 0\n \n \n -1.285 -2.935 0 0 -0 0\n \n \n \n \n 3 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 3 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n -2.425 0 0 0 0 -1.5708\n 0\n 0\n \n \n \n \n \n 5 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 5 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 0 -1.425 0 0 -0 0\n 0\n 0\n \n \n \n \n \n 0.61 0.6 0.6\n \n \n 0 0 0.3 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.3 0 -0 0\n \n \n 0.61 0.6 0.6\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n -0.33913 -0.815869 0 0 -0 0\n 0\n 0\n \n \n \n \n \n 1.4 0.3 1.5\n \n \n 0 0 0.75 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.75 0 -0 0\n \n \n 1.4 0.3 1.5\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n 1.4297 -0.665956 0 0 -0 1.5708\n 0\n 0\n \n \n \n \n \n 3 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 3 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 2.425 0 0 0 -0 1.5708\n 0\n 0\n \n \n \n \n \n 5 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 5 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 0 1.425 0 0 -0 3.14159\n 0\n 0\n \n \n \n \n \n 0.61 0.6 0.6\n \n \n 0 0 0.3 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.3 0 -0 0\n \n \n 0.61 0.6 0.6\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n -1.36413 0.401739 0 0 0 -1.5708\n 0\n 0\n \n \n \n \n \n 0.61 0.6 0.6\n \n \n 0 0 0.3 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.3 0 -0 0\n \n \n 0.61 0.6 0.6\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n 0.449131 0.8 0 0 -0 0\n 0\n 0\n \n 1\n \n \n 4375 659000000\n 434 515193556\n 1554033156 868355013\n 433385\n \n -1.285 -2.935 0 0 -0 0\n 1 1 1\n \n -3.71 -2.935 0 0 0 -1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -1.285 -4.36 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -2.085 -3.935 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n 0.1447 -3.60096 0 0 -0 1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n 1.14 -2.935 0 0 -0 1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -1.285 -1.51 0 0 -0 3.14159\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -2.785 -1.935 0 0 0 -1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -1.185 -2.535 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n \n 0 0 0 0 -0 0\n 1 1 1\n \n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n \n 0 0 10 0 -0 0\n \n \n \n \n -1.68505 -3.58802 10.7787 1e-05 1.5698 0.180161\n orbit\n perspective\n \n \n \n\n" }, { "path": "rl_nav/worlds/maze3.world", "content": "\n \n \n 1\n 0 0 10 0 -0 0\n 0.8 0.8 0.8 1\n 0.2 0.2 0.2 1\n \n 1000\n 0.9\n 0.01\n 0.001\n \n -0.5 0.1 -0.9\n \n \n 1\n \n \n \n \n 0 0 1\n 100 100\n \n \n \n \n \n 100\n 50\n \n \n \n \n \n \n \n \n \n \n 10\n \n \n 0\n \n \n 0 0 1\n 100 100\n \n \n \n \n \n \n 0\n 0\n \n \n 0 0 -9.8\n 6e-06 2.3e-05 -4.2e-05\n \n \n 0.001\n 1\n 1000\n \n \n 0.4 0.4 0.4 1\n 0.7 0.7 0.7 1\n 1\n \n \n EARTH_WGS84\n 0\n 0\n 0\n 0\n \n \n -1.835 -3.095 0 0 -0 0\n \n \n \n \n 3 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 3 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n -2.425 0 0 0 0 -1.5708\n 0\n 0\n \n \n \n \n \n 5 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 5 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 0 -1.425 0 0 -0 0\n 0\n 0\n \n \n \n \n \n 1.4 0.3 1.5\n \n \n 0 0 0.75 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.75 0 -0 0\n \n \n 1.4 0.3 1.5\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n 1.205 -0.015 0 0 -0 1.5708\n 0\n 0\n \n \n \n \n \n 3 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 3 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 2.425 0 0 0 -0 1.5708\n 0\n 0\n \n \n \n \n \n 5 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 5 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 0 1.425 0 0 -0 3.14159\n 0\n 0\n \n \n \n \n \n 0.61 0.6 0.6\n \n \n 0 0 0.3 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.3 0 -0 0\n \n \n 0.61 0.6 0.6\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n -0.125 0.56 0 0 0 -1.5708\n 0\n 0\n \n \n \n \n \n 0.61 0.6 0.6\n \n \n 0 0 0.3 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.3 0 -0 0\n \n \n 0.61 0.6 0.6\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n -1.34 -0.455 0 0 -0 0\n 0\n 0\n \n \n \n \n \n 1.65 0.3 1.5\n \n \n 0 0 0.75 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.75 0 -0 0\n \n \n 1.65 0.3 1.5\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n 0.53 -0.565 0 0 -0 0\n 0\n 0\n \n 1\n \n \n 271 833000000\n 272 611875126\n 1551698617 605584036\n 271833\n \n -1.835 -3.095 0 0 -0 0\n 1 1 1\n \n -4.26 -3.095 0 0 0 -1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -1.835 -4.52 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -0.63 -3.11 0 0 -0 1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n 0.59 -3.095 0 0 -0 1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -1.835 -1.67 0 0 -0 3.14159\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -1.96 -2.535 0 0 0 -1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -3.175 -3.55 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -1.305 -3.66 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n \n 0 0 0 0 -0 0\n 1 1 1\n \n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n \n 0 0 10 0 -0 0\n \n \n \n \n -1.48842 -3.18562 9.09859 -3e-06 1.5578 1.52018\n orbit\n perspective\n \n \n \n\n" }, { "path": "rl_nav/worlds/maze4.world", "content": "\n \n \n 1\n 0 0 10 0 -0 0\n 0.8 0.8 0.8 1\n 0.2 0.2 0.2 1\n \n 1000\n 0.9\n 0.01\n 0.001\n \n -0.5 0.1 -0.9\n \n \n 1\n \n \n \n \n 0 0 1\n 100 100\n \n \n \n \n \n 100\n 50\n \n \n \n \n \n \n \n \n \n \n 10\n \n \n 0\n \n \n 0 0 1\n 100 100\n \n \n \n \n \n \n 0\n 0\n \n \n 0 0 -9.8\n 6e-06 2.3e-05 -4.2e-05\n \n \n 0.001\n 1\n 1000\n \n \n 0.4 0.4 0.4 1\n 0.7 0.7 0.7 1\n 1\n \n \n EARTH_WGS84\n 0\n 0\n 0\n 0\n \n \n -2.505 -2.875 0 0 -0 0\n \n \n \n \n 3 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 3 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n -2.425 0 0 0 0 -1.5708\n 0\n 0\n \n \n \n \n \n 5 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 5 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 0 -1.425 0 0 -0 0\n 0\n 0\n \n \n \n \n \n 3 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 3 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 2.425 0 0 0 -0 1.5708\n 0\n 0\n \n \n \n \n \n 1.25 0.3 1.5\n \n \n 0 0 0.75 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.75 0 -0 0\n \n \n 1.25 0.3 1.5\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n -1.10133 -0.494544 0 0 0 -0.523599\n 0\n 0\n \n \n \n \n \n 1.25 0.3 1.5\n \n \n 0 0 0.75 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.75 0 -0 0\n \n \n 1.25 0.3 1.5\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n 1.01138 -0.433674 0 0 -0 0.523599\n 0\n 0\n \n \n \n \n \n 1.19196 0.3 1.5\n \n \n 0 0 0.75 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.75 0 -0 0\n \n \n 1.19196 0.3 1.5\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n -1.33172 0.154318 0 0 -0 1.17418\n 0\n 0\n \n \n \n \n \n 5 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 5 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 0 1.425 0 0 -0 3.14159\n 0\n 0\n \n \n \n \n \n 1.25 0.3 1.5\n \n \n 0 0 0.75 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.75 0 -0 0\n \n \n 1.25 0.3 1.5\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n 1.17654 0.215188 0 0 -0 2.0944\n 0\n 0\n \n \n \n \n \n 0.61 0.6 0.6\n \n \n 0 0 0.3 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.3 0 -0 0\n \n \n 0.61 0.6 0.6\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n -0.084699 0.086576 0 0 0 -1.5708\n 0\n 0\n \n 1\n \n \n 98 642000000\n 98 978539109\n 1551696170 583990469\n 98642\n \n -2.505 -2.875 0 0 -0 0\n 1 1 1\n \n -4.93 -2.875 0 0 0 -1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -2.505 -4.3 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -0.08 -2.875 0 0 -0 1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -3.60633 -3.36954 0 0 0 -0.523599\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -1.49362 -3.30867 0 0 -0 0.523599\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -3.83672 -2.72068 0 0 -0 1.17418\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -2.505 -1.45 0 0 -0 3.14159\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -1.32846 -2.65981 0 0 -0 2.0944\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -2.5897 -2.78842 0 0 0 -1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n \n 0 0 0 0 -0 0\n 1 1 1\n \n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n \n 0 0 10 0 -0 0\n \n \n \n \n -2.41298 -2.8544 7.30521 -3e-06 1.5578 1.55238\n orbit\n perspective\n \n \n \n\n" }, { "path": "rl_nav/worlds/maze5.world", "content": "\n \n \n 1\n 0 0 10 0 -0 0\n 0.8 0.8 0.8 1\n 0.2 0.2 0.2 1\n \n 1000\n 0.9\n 0.01\n 0.001\n \n -0.5 0.1 -0.9\n \n \n 1\n \n \n \n \n 0 0 1\n 100 100\n \n \n \n \n \n 100\n 50\n \n \n \n \n \n \n \n \n \n \n 10\n \n \n 0\n \n \n 0 0 1\n 100 100\n \n \n \n \n \n \n 0\n 0\n \n \n 0 0 -9.8\n 6e-06 2.3e-05 -4.2e-05\n \n \n 0.001\n 1\n 1000\n \n \n 0.4 0.4 0.4 1\n 0.7 0.7 0.7 1\n 1\n \n \n EARTH_WGS84\n 0\n 0\n 0\n 0\n \n \n -1.935 -2.965 0 0 -0 0\n \n \n \n \n 3 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 3 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n -2.425 0 0 0 0 -1.5708\n 0\n 0\n \n \n \n \n \n 5 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 5 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 0 -1.425 0 0 -0 0\n 0\n 0\n \n \n \n \n \n 0.61 0.6 0.6\n \n \n 0 0 0.3 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.3 0 -0 0\n \n \n 0.61 0.6 0.6\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n 1.14442 -0.577004 0 0 -0 0\n 0\n 0\n \n \n \n \n \n 1.67977 0.3 1.5\n \n \n 0 0 0.75 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.75 0 -0 0\n \n \n 1.67977 0.3 1.5\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n -0.108 -0.288114 0 0 -0 1.5708\n 0\n 0\n \n \n \n \n \n 1.15 0.3 1.5\n \n \n 0 0 0.75 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.75 0 -0 0\n \n \n 1.15 0.3 1.5\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n -0.533 0.394212 0 0 -0 3.14159\n 0\n 0\n \n \n \n \n \n 3 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 3 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 2.425 0 0 0 -0 1.5708\n 0\n 0\n \n \n \n \n \n 1.40035 0.3 1.5\n \n \n 0 0 0.75 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.75 0 -0 0\n \n \n 1.40035 0.3 1.5\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n 1.742 0.58 0 0 -0 0\n 0\n 0\n \n \n \n \n \n 5 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 5 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 0 1.425 0 0 -0 3.14159\n 0\n 0\n \n \n \n \n \n 0.61 0.6 0.6\n \n \n 0 0 0.3 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.3 0 -0 0\n \n \n 0.61 0.6 0.6\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n -1.30379 -0.828318 0 0 0 -1.5708\n 0\n 0\n \n 1\n \n \n 63 678000000\n 63 909182304\n 1551700367 6970790\n 63678\n \n -1.935 -2.965 0 0 -0 0\n 1 1 1\n \n -4.36 -2.965 0 0 0 -1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -1.935 -4.39 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -0.79058 -3.542 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -2.043 -3.25311 0 0 -0 1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -2.468 -2.57079 0 0 -0 3.14159\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n 0.49 -2.965 0 0 -0 1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -0.193 -2.385 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -1.935 -1.54 0 0 -0 3.14159\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -3.23879 -3.79332 0 0 0 -1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n \n 0 0 0 0 -0 0\n 1 1 1\n \n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n \n 0 0 10 0 -0 0\n \n \n \n \n -1.98786 -3.27849 8.69596 0 1.5378 1.61215\n orbit\n perspective\n \n \n \n\n" }, { "path": "rl_nav/worlds/maze6.world", "content": "\n \n \n 1\n 0 0 10 0 -0 0\n 0.8 0.8 0.8 1\n 0.2 0.2 0.2 1\n \n 1000\n 0.9\n 0.01\n 0.001\n \n -0.5 0.1 -0.9\n \n \n 1\n \n \n \n \n 0 0 1\n 100 100\n \n \n \n \n \n 100\n 50\n \n \n \n \n \n \n \n \n \n \n 10\n \n \n 0\n \n \n 0 0 1\n 100 100\n \n \n \n \n \n \n 0\n 0\n \n \n 0 0 -9.8\n 6e-06 2.3e-05 -4.2e-05\n \n \n 0.001\n 1\n 1000\n \n \n 0.4 0.4 0.4 1\n 0.7 0.7 0.7 1\n 1\n \n \n EARTH_WGS84\n 0\n 0\n 0\n 0\n \n \n -1.43 -3.295 0 0 -0 0\n \n \n \n \n 3 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 3 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n -2.43 0 0 0 0 -1.5708\n 0\n 0\n \n \n \n \n \n 5.01 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 5.01 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 0 -1.425 0 0 -0 0\n 0\n 0\n \n \n \n \n \n 1.71039 0.3 1.5\n \n \n 0 0 0.75 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.75 0 -0 0\n \n \n 1.71039 0.3 1.5\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n 0.536 -0.6485 0 0 0 -0.004964\n 0\n 0\n \n \n \n \n \n 3 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 3 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 2.43 0 0 0 -0 1.5708\n 0\n 0\n \n \n \n \n \n 5.01 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 5.01 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 0 1.425 0 0 -0 3.14159\n 0\n 0\n \n \n \n \n \n 0.61 0.6 0.6\n \n \n 0 0 0.3 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.3 0 -0 0\n \n \n 0.61 0.6 0.6\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n -1.27316 0.730299 0 0 0 -1.5708\n 0\n 0\n \n \n \n \n \n 0.61 0.6 0.6\n \n \n 0 0 0.3 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.3 0 -0 0\n \n \n 0.61 0.6 0.6\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n -1.25325 -0.65758 0 0 -0 1.5708\n 0\n 0\n \n \n \n \n \n 2.15053 0.3 1.5\n \n \n 0 0 0.75 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.75 0 -0 0\n \n \n 2.15053 0.3 1.5\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n 0.489 0.0055 0 0 0 -2.36193\n 0\n 0\n \n 1\n \n \n 241 276000000\n 241 990139298\n 1551696900 588130540\n 241276\n \n -1.43 -3.295 0 0 -0 0\n 1 1 1\n \n -3.86 -3.295 0 0 0 -1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -1.43 -4.72 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -0.894 -3.9435 0 0 0 -0.004964\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n 1 -3.295 0 0 -0 1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -1.43 -1.87 0 0 -0 3.14159\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -2.70316 -2.5647 0 0 0 -1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -2.68325 -3.95258 0 0 -0 1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -0.941 -3.2895 0 0 0 -2.36193\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n \n 0 0 0 0 -0 0\n 1 1 1\n \n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n \n 0 0 10 0 -0 0\n \n \n \n \n 0.201852 -2.725 11.2681 2.5e-05 1.5698 1.49607\n orbit\n perspective\n \n \n \n\n" }, { "path": "rl_nav/worlds/maze7.world", "content": "\n \n \n 1\n 0 0 10 0 -0 0\n 0.8 0.8 0.8 1\n 0.2 0.2 0.2 1\n \n 1000\n 0.9\n 0.01\n 0.001\n \n -0.5 0.1 -0.9\n \n \n 1\n \n \n \n \n 0 0 1\n 100 100\n \n \n \n \n \n 100\n 50\n \n \n \n \n \n \n \n \n \n \n 10\n \n \n 0\n \n \n 0 0 1\n 100 100\n \n \n \n \n \n \n 0\n 0\n \n \n 0 0 -9.8\n 6e-06 2.3e-05 -4.2e-05\n \n \n 0.001\n 1\n 1000\n \n \n 0.4 0.4 0.4 1\n 0.7 0.7 0.7 1\n 1\n \n \n EARTH_WGS84\n 0\n 0\n 0\n 0\n \n \n -3.0605 -5.811 0 0 -0 0\n \n \n \n \n 3 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 3 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n -2.4255 0 0 0 0 -1.5708\n 0\n 0\n \n \n \n \n \n 5 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 5 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 0 -1.425 0 0 -0 0\n 0\n 0\n \n \n \n \n \n 3 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 3 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 2.4255 0 0 0 -0 1.5708\n 0\n 0\n \n \n \n \n \n 5 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 5 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 0 1.425 0 0 -0 3.14159\n 0\n 0\n \n \n \n \n \n 0.61 0.6 0.6\n \n \n 0 0 0.3 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.3 0 -0 0\n \n \n 0.61 0.6 0.6\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n -1.22136 -0.386987 0 0 0 -1.5708\n 0\n 0\n \n \n \n \n \n 0.61 0.6 0.6\n \n \n 0 0 0.3 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.3 0 -0 0\n \n \n 0.61 0.6 0.6\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n 1.08529 -0.794049 0 0 0 -1.5708\n 0\n 0\n \n \n \n \n \n 1.4 0.3 1.5\n \n \n 0 0 0.75 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.75 0 -0 0\n \n \n 1.4 0.3 1.5\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n -0.0365 -0.013 0 0 -0 1.5708\n 0\n 0\n \n \n \n \n \n 1.40005 0.3 1.5\n \n \n 0 0 0.75 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.75 0 -0 0\n \n \n 1.40005 0.3 1.5\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n 0.5135 0.537 0 0 -0 0\n 0\n 0\n \n 1\n \n \n 21 736000000\n 21 838781296\n 1551710274 614794365\n 21736\n \n -3.0605 -5.811 0 0 -0 0\n 1 1 1\n \n -5.486 -5.811 0 0 0 -1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -3.0605 -7.236 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -0.635 -5.811 0 0 -0 1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -3.0605 -4.386 0 0 -0 3.14159\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -4.28186 -6.19799 0 0 0 -1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -1.97521 -6.60505 0 0 0 -1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -3.097 -5.824 0 0 -0 1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -2.547 -5.274 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n \n 0 0 0 0 -0 0\n 1 1 1\n \n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n \n 0 0 10 0 -0 0\n \n \n \n \n -2.38029 -6.27059 9.93705 -5e-05 1.5698 1.6242\n orbit\n perspective\n \n \n \n\n" }, { "path": "rl_nav/worlds/maze8.world", "content": "\n \n \n 1\n 0 0 10 0 -0 0\n 0.8 0.8 0.8 1\n 0.2 0.2 0.2 1\n \n 1000\n 0.9\n 0.01\n 0.001\n \n -0.5 0.1 -0.9\n \n \n 1\n \n \n \n \n 0 0 1\n 100 100\n \n \n \n \n \n 100\n 50\n \n \n \n \n \n \n \n \n \n \n 10\n \n \n 0\n \n \n 0 0 1\n 100 100\n \n \n \n \n \n \n 0\n 0\n \n \n 0 0 -9.8\n 6e-06 2.3e-05 -4.2e-05\n \n \n 0.001\n 1\n 1000\n \n \n 0.4 0.4 0.4 1\n 0.7 0.7 0.7 1\n 1\n \n \n EARTH_WGS84\n 0\n 0\n 0\n 0\n \n \n -2.58484 -3.11806 0 0 -0 0\n \n \n \n \n 2.96033 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 2.96033 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n -2.41839 0 0 0 0 -1.5708\n 0\n 0\n \n \n \n \n \n 5 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 5 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 0.006612 -1.40516 0 0 -0 0\n 0\n 0\n \n \n \n \n \n 1.5 0.3 1.5\n \n \n 0 0 0.75 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.75 0 -0 0\n \n \n 1.5 0.3 1.5\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n 0.453011 -0.557155 0 0 -0 0\n 0\n 0\n \n \n \n \n \n 1.5 0.3 1.5\n \n \n 0 0 0.75 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.75 0 -0 0\n \n \n 1.5 0.3 1.5\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n 1.04432 0.042845 0 0 -0 1.5708\n 0\n 0\n \n \n \n \n \n 2.96033 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 2.96033 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n 2.43161 0 0 0 -0 1.5708\n 0\n 0\n \n \n \n \n \n 5 0.15 2.5\n \n \n 0 0 1.25 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 1.25 0 -0 0\n \n \n 5 0.15 2.5\n \n \n \n \n 0.760784 0.662745 0.627451 1\n \n \n 0\n \n \n -0.006613 1.40516 0 0 -0 3.14159\n 0\n 0\n \n \n \n \n \n 0.61 0.6 0.6\n \n \n 0 0 0.3 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.3 0 -0 0\n \n \n 0.61 0.6 0.6\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n -0.441144 0.583523 0 0 -0 3.14159\n 0\n 0\n \n \n \n \n \n 0.61 0.6 0.6\n \n \n 0 0 0.3 0 -0 0\n 10\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 0 0 0.3 0 -0 0\n \n \n 0.61 0.6 0.6\n \n \n \n \n 1 1 1 1\n \n \n 0\n \n \n -1.43716 -1.04194 0 0 -0 1.5708\n 0\n 0\n \n 1\n \n \n 38 659000000\n 38 816752131\n 1551711734 170197512\n 38659\n \n -2.58484 -3.11806 0 0 -0 0\n 1 1 1\n \n -5.00323 -3.11806 0 0 0 -1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -2.57823 -4.52322 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -2.13183 -3.67522 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -1.54052 -3.07522 0 0 -0 1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -0.15323 -3.11806 0 0 -0 1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -2.59145 -1.7129 0 0 -0 3.14159\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -3.02598 -2.53454 0 0 -0 3.14159\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n -4.022 -4.16 0 0 -0 1.5708\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n \n 0 0 0 0 -0 0\n 1 1 1\n \n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n 0 0 0 0 -0 0\n \n \n \n 0 0 10 0 -0 0\n \n \n \n \n -1.76128 -3.04307 10.0562 -7e-06 1.5698 1.62427\n orbit\n perspective\n \n \n \n\n" }, { "path": "tools/calculate_SPL.py", "content": "#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n\nimport matplotlib.pyplot as plt\n\n\ndef main():\n recordpath = '../materials/record/'\n filepath = recordpath + 'PPO_nav1.txt'\n f = open(filepath, 'r')\n lines = f.readlines()\n last100 = lines[-100:]\n successes = []\n rewards = []\n timesteps = []\n shortest = 1000\n\n for i in range(100):\n line = last100[i] # eg: '[432.1290540951935, 248, True]'\n data = line.split()\n rewards.append(float(data[0][1:-1])) # 432.1290540951935\n timesteps.append(float(data[1][:-1])) # 248\n # successes.append(bool(data[2][:-1])) # bool('False') is True!\n successes.append(data[2][:-1] == str('True'))\n if successes[i]:\n if timesteps[i] < shortest:\n shortest = timesteps[i]\n print('success rate: {}%'.format(sum(successes)))\n print('the shortest path distance: {}'.format(shortest))\n\n SPL = 0\n for i in range(100):\n if successes[i]:\n SPL += 1.0/100*shortest/max(timesteps[i], shortest)\n print('SPL is {}'.format(SPL))\n\n plt.xlabel('episode')\n plt.ylabel('reward')\n plt.scatter(range(100), rewards, s=8, color='green')\n plt.legend(loc='best')\n plt.title('End-to-end PPO performance')\n plt.show()\n\n f.close()\n\n\nmain()\n" }, { "path": "tools/draw_reward.py", "content": "#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nplt.rcParams['font.sans-serif'] = ['Times New Roman'] # 如果要显示中文字体,则在此处设为：SimHei\nplt.rcParams['axes.unicode_minus'] = False # 显示负号\nplt.figure(figsize=(10, 6))\nplt.grid(linestyle=\"--\") # 设置背景网格线为虚线\nax = plt.gca()\nax.spines['top'].set_visible(False) # 去掉上边框\nax.spines['right'].set_visible(False) # 去掉右边框\n\nrecord_path = '../materials/record/'\n\n\ndef main():\n f = open(record_path + 'PPO_nav1.txt', 'r')\n lines = f.readlines()\n reward = []\n rewards = []\n\n for i in range(0, len(lines)-50, 50):\n for j in range(50):\n line = lines[i+j] # eg: '[432.1290540951935, 248, True]'\n data = line.split()\n reward.append(float(data[0][1:-1])) # 432.1290540951935\n avg_reward = np.mean(reward)\n rewards.append(avg_reward)\n reward = []\n\n f.close()\n\n f2 = open(record_path + 'E2E_PPO_nav1.txt', 'r')\n lines2 = f2.readlines()\n reward2 = []\n rewards2 = []\n\n for i in range(0, len(lines2)-50, 50):\n for j in range(50):\n line2 = lines2[i+j] # eg: '[432.1290540951935, 248, True]'\n data2 = line2.split()\n reward2.append(float(data2[0][1:-1])) # 432.1290540951935\n avg_reward2 = np.mean(reward2)\n rewards2.append(avg_reward2)\n reward2 = []\n\n f2.close()\n\n plt.plot(range(len(rewards)), rewards, color=\"blue\", label=\"Proposed\", linewidth=1.5)\n plt.plot(range(len(rewards2)), rewards2, color=\"green\", label=\"Baseline\", linewidth=1.5)\n\n size = 22\n plt.xticks(fontsize=size) # 默认字体大小为10\n plt.yticks(fontsize=size)\n # plt.title(\"example\", fontsize=12, fontweight='bold') # 默认字体大小为12\n plt.xlabel(\"50*episodes\", fontsize=size)\n plt.ylabel(\"average reward\", fontsize=size)\n\n plt.title('maze1', fontsize=size)\n # plt.legend() # 显示各曲线的图例\n plt.legend(loc=4, numpoints=1) # lower right\n leg = plt.gca().get_legend()\n ltext = leg.get_texts()\n plt.setp(ltext, fontsize=size) # 设置图例字体的大小和粗细\n \n axes = plt.gca()\n # axes.set_xlim([None, None]) # 限定X轴范围\n\n plt.savefig('../result/maze1_dense_reward.png')\n plt.show()\n\n\nmain()\n" }, { "path": "tools/draw_success_rate.py", "content": "#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n\nimport matplotlib.pyplot as plt\n\nplt.rcParams['font.sans-serif'] = ['Times New Roman'] # 如果要显示中文字体,则在此处设为：SimHei\nplt.rcParams['axes.unicode_minus'] = False # 显示负号\nplt.figure(figsize=(10, 6))\nplt.grid(linestyle=\"--\") # 设置背景网格线为虚线\nax = plt.gca()\nax.spines['top'].set_visible(False) # 去掉上边框\nax.spines['right'].set_visible(False) # 去掉右边框\n\nrecord_path = '../materials/record/'\n\n\ndef main():\n f = open(record_path + 'PPO_nav1.txt', 'r')\n lines = f.readlines()\n success = []\n successes = []\n\n for i in range(len(lines)):\n if i < 100:\n success.append(0)\n else:\n for j in range(100):\n line = lines[i-j] # eg: '[432.1290540951935, 248, True]'\n data = line.split()\n # successes.append(bool(data[2][:-1])) # bool('False') is True!\n success.append(data[2][:-1] == str('True'))\n success_rate = sum(success)\n successes.append(success_rate)\n success = []\n\n f.close()\n\n f2 = open(record_path + 'E2E_PPO_nav1.txt', 'r')\n lines2 = f2.readlines()\n success2 = []\n successes2 = []\n\n for i in range(100, len(lines2)):\n if i < 100:\n success2.append(0)\n else:\n for j in range(100):\n line2 = lines2[i-j] # eg: '[432.1290540951935, 248, True]'\n data2 = line2.split()\n # successes.append(bool(data[2][:-1])) # bool('False') is True!\n success2.append(data2[2][:-1] == str('True'))\n success_rate2 = sum(success2)\n successes2.append(success_rate2)\n success2 = []\n\n f2.close()\n\n plt.plot(range(len(successes)), successes, color=\"blue\", label=\"Proposed\", linewidth=1.5)\n plt.plot(range(len(successes2)), successes2, color=\"green\", label=\"Baseline\", linewidth=1.5)\n\n size = 22\n plt.xticks(fontsize=size) # 默认字体大小为10\n plt.yticks(fontsize=size)\n # plt.title(\"example\", fontsize=12, fontweight='bold') # 默认字体大小为12\n plt.xlabel(\"episode\", fontsize=size)\n plt.ylabel(\"success rate(%)\", fontsize=size)\n\n plt.title('maze1', fontsize=size)\n # plt.legend() # 显示各曲线的图例\n plt.legend(loc=4, numpoints=1) # lower right\n leg = plt.gca().get_legend()\n ltext = leg.get_texts()\n plt.setp(ltext, fontsize=size) # 设置图例字体的大小和粗细\n \n axes = plt.gca()\n # axes.set_xlim([None, None]) # 限定X轴的范围\n\n plt.savefig('../result/maze1_dense_success.png')\n plt.show()\n\n\nmain()\n" }, { "path": "turtlebot_description/CMakeLists.txt", "content": "cmake_minimum_required(VERSION 2.8.3)\n\nproject(turtlebot_description)\nfind_package(catkin REQUIRED COMPONENTS urdf xacro)\n\ncatkin_package(\n CATKIN_DEPENDS urdf xacro\n)\n\ninstall(DIRECTORY robots\n DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION}\n)\ninstall(DIRECTORY meshes\n DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION}\n)\ninstall(DIRECTORY test\n DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION}\n)\ninstall(DIRECTORY urdf\n DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION}\n)\n" }, { "path": "turtlebot_description/meshes/sensors/kinect.dae", "content": "\n\n \n \n DesignK\n OpenCOLLADA for 3ds Max; Version: 1.3.1; Revision: 847M; Platform: x64; Configuration: Release_Max2011_static\n file:///E:/Windows/Documents/3dsMax/scenes/KOBUKI_KINECT_URDF.max\n \n 2013-03-07T11:38:26\n 2013-03-07T11:38:26\n \n \n \n \n \n \n \n kinect_jpg\n \n \n \n \n kinect_jpg-surface\n \n \n \n \n \n 0 0 0 1\n \n \n 0.5882353 0.5882353 0.5882353 1\n \n \n \n \n \n 0.9 0.9 0.9 1\n \n \n 9.999999\n \n \n 0 0 0 1\n \n \n 1 1 1 1\n \n \n 1\n \n \n \n \n \n \n \n 0\n 0\n 0\n 1.5\n 0\n 3\n 1\n 0\n \n \n 1\n 1\n 0\n 0.1\n 0\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n -0.1061134 0.05260616 -0.02802278 -0.1352909 0.05260616 0.02597721 0.1352934 0.05260616 0.02597721 -0.1352909 0.05256529 0.02654758 0.106116 0.05260616 -0.02802278 0.1357952 0.05256529 0.02570608 0.135778 0.05256617 0.02626617 0.1352934 0.05256529 0.02654759 -0.1066147 0.05256538 -0.02829361 -0.1357927 0.05256529 0.02570608 -0.1357754 0.05256617 0.02626617 0.106116 0.05256538 -0.02859251 0.03910309 -0.0170235 0.003464307 0.03902168 -0.01714947 0.002144386 0.03902543 -0.0170235 0.00610285 0.03897252 -0.01714947 0.004780651 -0.13881 0.01860616 0.02407574 -0.13881 0.04860616 0.02407574 -0.1096326 0.04860616 -0.02992426 -0.1388008 0.04889608 0.02408074 -0.1096326 0.01860616 -0.02992426 -0.109346 0.04860616 -0.03037875 -0.1096237 0.04889023 -0.02991946 -0.1390514 0.01860616 0.0246139 -0.1390514 0.04860616 0.0246139 -0.1096237 0.0183221 -0.02991946 -0.1388009 0.01831803 0.02408068 -0.109346 0.01860616 -0.03037875 -0.1093304 0.01813386 -0.0303526 -0.1061134 0.01860616 -0.03202279 -0.1061134 0.04860616 -0.03202279 0.106116 0.04860616 -0.03202279 -0.1061134 0.04889023 -0.03201269 0.106116 0.01860616 -0.03202279 0.1066521 0.04860616 -0.0319867 0.106116 0.04889023 -0.03201269 -0.1066463 0.01860616 -0.03198713 -0.1066463 0.04860616 -0.03198713 -0.1066309 0.04907678 -0.03196116 0.106116 0.0183221 -0.03201269 -0.1061134 0.0183221 -0.03201269 0.1066521 0.01860616 -0.0319867 0.1066365 0.01813386 -0.03196055 0.1096352 0.01860616 -0.02992426 0.1096352 0.04860616 -0.02992426 0.1388126 0.04860616 0.02407574 0.1096263 0.04889023 -0.02991946 0.1388126 0.01860616 0.02407574 0.1390539 0.04860616 0.0246139 0.1388033 0.04889608 0.02408074 0.1093505 0.01860616 -0.03037614 0.1093505 0.04860616 -0.03037614 0.109335 0.04907678 -0.03035017 0.1388034 0.01831803 0.02408068 0.1096263 0.0183221 -0.02991946 0.1390539 0.01860616 0.0246139 0.1352934 0.01860616 0.02997722 0.1352934 0.04860616 0.02997722 -0.1352909 0.04860616 0.02997722 0.1352934 0.04889608 0.0299667 -0.1352909 0.01860616 0.02997722 -0.1358791 0.04860616 0.02993373 -0.1352909 0.04889608 0.0299667 0.1358816 0.01860616 0.02993373 0.1358816 0.04860616 0.02993373 -0.1352909 0.01831803 0.02996682 0.1352934 0.01831803 0.02996682 -0.1358791 0.01860616 0.02993373 -0.1352909 0.01460616 0.02597721 -0.001032712 0.01460616 0.007413065 1.28414e-6 0.01460616 0.007477214 0.001035281 0.01460616 0.007413065 0.001033153 0.006606162 0.007413671 -0.002034773 0.01460616 0.007229728 -0.003011256 0.01460616 0.006924568 -0.003950778 0.01460616 0.006502301 -0.003012618 0.006606162 0.00692422 -0.004825159 0.01460616 0.005973211 -0.005636773 0.01460616 0.005337587 -0.006359089 0.01460616 0.004615271 -0.006994712 0.01460616 0.003803658 -0.006360551 0.006606162 0.004613308 -0.007523802 0.01460616 0.002929276 -0.007946069 0.01460616 0.001989754 -0.008251283 0.01460616 0.001012861 -0.008435887 0.01460616 1.1794e-6 -0.008251064 0.006606162 0.001011235 -0.008498716 0.01460616 -0.001022786 -0.1061134 0.01460616 -0.02802278 -0.008435887 0.01460616 -0.002046751 -0.008251283 0.01460616 -0.003058433 -0.007946069 0.01460616 -0.004035325 -0.008251064 0.006606162 -0.003056807 -0.007523802 0.01460616 -0.004974848 -0.006994712 0.01460616 -0.00584923 -0.006359089 0.01460616 -0.006660843 -0.005636773 0.01460616 -0.007383159 -0.006360551 0.006606162 -0.00665888 -0.004825159 0.01460616 -0.008018782 -0.003950778 0.01460616 -0.008547872 -0.003011256 0.01460616 -0.00897014 -0.002034773 0.01460616 -0.009275299 -0.003012618 0.006606162 -0.008969792 -0.001032712 0.01460616 -0.009458637 0.106116 0.01460616 -0.02802278 1.28414e-6 0.01460616 -0.009522786 0.001035281 0.01460616 -0.009458637 0.002037341 0.01460616 -0.009275299 0.001033153 0.006606162 -0.009459243 0.003013824 0.01460616 -0.00897014 0.003953347 0.01460616 -0.008547872 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\n \n \n \n \n \n \n \n \n \n

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1982 2655 2381 1661 2520 1668 1981 2656 2381 1661 2520 2382 1659 2518 1667 1984 2657 1668 1981 2656 2382 1659 2518 1666 1985 2674 1667 1984 2657 2382 1659 2518 1666 1985 2674 2382 1659 2518 2383 1658 2517 1665 1986 2675 1666 1985 2674 2383 1658 2517 1665 1986 2675 2383 1658 2517 2384 1655 2502 1664 1993 2676 2384 1655 2502 2385 2655 2625 1664 1993 2676 1665 1986 2675 2384 1655 2502 1663 1994 2677 1664 1993 2676 2385 2655 2625 1663 1994 2677 2385 2655 2625 2386 2656 2642 1662 1995 2678 2386 2656 2642 2387 2657 2643 1662 1995 2678 1663 1994 2677 2386 2656 2642 1661 1980 2679 2387 2657 2643 2388 2658 2644 1661 1980 2679 1662 1995 2678 2387 2657 2643 1660 1979 2680 2388 2658 2644 2389 2659 2645 1660 1979 2680 1661 1980 2679 2388 2658 2644 1659 1978 2681 2389 2659 2645 2390 2660 2646 1659 1978 2681 1660 1979 2680 2389 2659 2645 1658 1977 2682 2390 2660 2646 2391 2661 2647 1658 1977 2682 1659 1978 2681 2390 2660 2646 1657 1976 2683 2391 2661 2647 2392 2662 2648 1657 1976 2683 1658 1977 2682 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3131 1646 1987 3182 1647 1992 3179 2403 2673 3129 1645 1969 3183 1646 1987 3182 2404 2674 3131 1645 1969 3183 2404 2674 3131 2405 2675 3133 1644 1968 3185 1645 1969 3183 2405 2675 3133 1644 1968 3185 2405 2675 3133 2406 2676 3155 1643 1967 3077 1644 1968 3185 2406 2676 3155 1643 1967 3077 2406 2676 3155 2374 2647 2406 1669 1982 2655 2413 2683 2684 2377 2654 2624 2412 2682 2654 2377 2654 2624 2413 2683 2684 1701 2015 3067 1695 2019 3074 2418 2684 3087 1669 1982 2655 1637 1983 2685 2413 2683 2684 2414 2685 2686 2413 2683 2684 1637 1983 2685 2415 2686 2687 2412 2682 2654 2413 2683 2684 2416 2687 2688 2415 2686 2687 2413 2683 2684 2414 2685 2686 2416 2687 2688 2413 2683 2684 1695 2019 3074 1696 2020 3076 2419 2688 3088 2420 2689 2689 2414 2685 2686 1637 1983 2685 2421 2690 2944 2420 2689 2945 1637 1983 2300 2421 2690 2944 1637 1983 2300 1638 1963 2280 1696 2020 3076 1712 1745 3075 2422 2691 3089 2423 1935 2235 2421 2690 2944 1638 1963 2280 2425 2692 2706 2376 1663 2522 2375 2648 2621 2425 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2414 2685 2686 2420 2689 2689 2437 2705 2718 2436 2703 2717 2414 2685 2686 2438 2706 2946 2420 2689 2945 2421 2690 2944 2438 2706 2719 2437 2705 2718 2420 2689 2689 1695 2019 3074 2419 2688 3088 2417 2704 3090 2439 2707 2947 2438 2706 2946 2421 2690 2944 1696 2020 3076 2422 2691 3089 2419 2688 3088 2440 2708 2948 2421 2690 2944 2423 1935 2235 2440 2708 2948 2439 2707 2947 2421 2690 2944 2441 1938 2251 2440 2708 2948 2423 1935 2235 2443 2709 2535 1471 1720 1576 1472 1719 1575 2443 2709 2535 2442 1722 1578 1471 1720 1576 2444 2710 2536 1472 1719 1575 1473 1717 1572 2444 2710 2536 2443 2709 2535 1472 1719 1575 2445 2711 2537 1473 2712 1572 1474 2713 1574 2445 2714 2537 2444 2710 2536 1473 1717 1572 2446 2715 2720 1474 1716 2588 1475 1715 2587 2446 2715 2720 2445 2714 2721 1474 1716 2588 2447 2716 2738 1475 1715 2587 1476 1713 2585 2447 2716 2738 2446 2715 2720 1475 1715 2587 2448 1711 2583 2447 2716 2738 1476 1713 2585 2463 2717 2538 2442 1722 1578 2443 2709 2535 2463 2717 2538 2462 1723 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2885 3128 3000 2853 3096 2999 2854 3097 3001 2885 3128 3000 2884 3127 2998 2853 3096 2999 2886 3129 3002 2854 3097 3001 2855 3098 3003 2886 3129 3002 2885 3128 3000 2854 3097 3001 2887 3130 3004 2855 3098 3003 2856 3099 3005 2887 3130 3004 2886 3129 3002 2855 3098 3003 2888 3131 3006 2856 3099 3005 2857 3100 3007 2888 3131 3006 2887 3130 3004 2856 3099 3005 2889 3132 3008 2857 3100 3007 2858 3101 3009 2889 3132 3008 2888 3131 3006 2857 3100 3007 2890 3133 3559 2858 3101 3009 2859 3102 2966 2890 3133 3559 2889 3132 3008 2858 3101 3009 2891 3134 3560 2859 3102 2966 2860 3103 2963 2891 3134 3560 2890 3133 3559 2859 3102 2966 2892 3135 3561 2860 3103 2963 2861 3104 2967 2892 3135 3561 2891 3134 3560 2860 3103 2963 2893 3136 3562 2861 3104 2967 2862 3105 2969 2893 3136 3562 2892 3135 3561 2861 3104 2967 2894 3137 3563 2862 3105 2969 2863 3106 2970 2894 3137 3563 2893 3136 3562 2862 3105 2969 2895 3138 3564 2863 3106 2970 2864 3107 2971 2895 3138 3564 2894 3137 3563 2863 3106 2970 2896 3139 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3182 3612 2938 3181 3611 2907 3150 3576 2940 3183 3613 2908 3151 3577 2909 3152 3578 2940 3183 3613 2939 3182 3612 2908 3151 3577 2941 3184 3614 2909 3152 3578 2910 3153 3580 2941 3184 3614 2940 3183 3613 2909 3152 3578 2942 3185 3615 2910 3153 3580 2911 3154 3582 2942 3185 3615 2941 3184 3614 2910 3153 3580 2943 3186 3616 2911 3154 3582 2912 3155 3584 2943 3186 3616 2942 3185 3615 2911 3154 3582 2944 3187 3617 2912 3155 3584 2913 3156 3586 2944 3187 3617 2943 3186 3616 2912 3155 3584 2945 3188 3618 2913 3156 3586 2439 2707 2947 2945 3188 3618 2944 3187 3617 2913 3156 3586 2440 2708 2948 2945 3188 3618 2439 2707 2947 1698 2022 2321 1699 2023 2322 2914 3157 3587 2946 3189 3619 2914 3157 3587 2915 3158 3588 2946 3189 3619 1698 2022 2321 2914 3157 3587 2947 3190 3620 2915 3158 3588 2916 3159 3589 2947 3190 3620 2946 3189 3619 2915 3158 3588 2948 3191 3621 2916 3159 3589 2917 3160 3590 2948 3191 3621 2947 3190 3620 2916 3159 3589 2949 3192 3622 2917 3160 3590 2918 3161 3591 2949 3192 3622 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3766 1970 1756 3786 2071 1758 3785 3198 3345 3765 1967 1757 3787 3199 3346 3766 3200 3347 3767 1967 1757 3787 1970 1756 3786 3199 3346 3766 1866 1718 3788 3200 3347 3767 3201 1755 3768 1866 1718 3788 1967 1757 3787 3200 3347 3767 1762 1753 3789 3203 3348 3770 3204 3349 3771 1762 1753 3789 3202 1754 3769 3203 3348 3770 1761 1751 3790 3204 3349 3771 3205 3350 3772 1761 1751 3790 1762 1753 3789 3204 3349 3771 1760 1752 3081 3205 3350 3772 3206 3351 3773 1760 1752 3081 1761 1751 3790 3205 3350 3772 1757 2077 3082 3206 3351 3773 3207 3352 3774 1757 2077 3082 1760 1752 3081 3206 3351 3773 1753 1750 3083 3207 3352 3774 3208 3353 3775 1753 1750 3083 1757 2077 3082 3207 3352 3774 1752 1749 3791 3208 3353 3775 3209 3354 3776 1752 1749 3791 1753 1750 3083 3208 3353 3775 1751 1748 3792 3209 3354 3776 3210 3355 3777 1751 1748 3792 1752 1749 3791 3209 3354 3776 1749 1747 3793 3210 3355 3777 3211 1746 3778 1749 1747 3793 1751 1748 3792 3210 3355 3777 1712 1745 3075 3211 1746 3778 3212 3356 3779 1712 1745 3075 3212 3356 3779 2422 2691 3089

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287 192 191 284 191 194 288 194 190 281 401 193 285 404 191 282 403 192 283 189 191 284 191 195 287 192 196 289 407 197 290 409 194 286 406 198 291 195 194 288 194 197 292 197 193 285 404 196 289 407 194 286 406 195 287 192 194 288 194 198 291 195 199 293 466 200 294 467 197 290 409 201 295 198 197 292 197 200 296 200 196 289 407 199 293 466 197 290 409 198 291 195 197 292 197 201 295 198 202 297 469 203 298 470 200 294 467 204 299 201 200 296 200 203 300 203 199 293 466 202 297 469 200 294 467 201 295 198 200 296 200 204 299 201 205 301 472 206 302 473 203 298 470 207 303 204 203 300 203 206 304 206 202 297 469 205 301 472 203 298 470 204 299 201 203 300 203 207 303 204 208 305 475 209 306 476 206 302 473 210 307 698 206 304 699 209 308 700 205 301 472 208 305 475 206 302 473 207 303 204 206 304 206 210 307 207 211 309 478 212 310 479 209 306 476 213 311 701 209 308 700 212 312 702 208 305 475 211 309 478 209 306 476 210 307 698 209 308 700 213 311 701 214 313 481 215 314 482 212 310 479 216 315 703 212 312 702 215 316 704 211 309 478 214 313 481 212 310 479 213 311 701 212 312 702 216 315 703 217 317 484 218 318 485 215 314 482 219 319 705 215 316 704 218 320 706 214 313 481 217 317 484 215 314 482 216 315 703 215 316 704 219 319 705 220 321 487 221 322 488 218 318 485 222 323 707 218 320 706 221 324 708 217 317 484 220 321 487 218 318 485 219 319 705 218 320 706 222 323 707 223 325 490 224 326 491 221 322 488 225 327 709 221 324 708 224 328 710 220 321 487 223 325 490 221 322 488 222 323 707 221 324 708 225 327 709 226 329 493 227 330 494 224 326 491 228 331 711 224 328 710 227 332 712 223 325 490 226 329 493 224 326 491 225 327 709 224 328 710 228 331 711 229 333 924 230 334 925 227 330 494 231 335 713 227 332 712 230 336 714 226 329 493 229 333 924 227 330 494 228 331 711 227 332 712 231 335 713 232 337 926 233 338 927 230 334 925 234 339 715 230 336 714 233 340 716 229 333 924 232 337 926 230 334 925 231 335 713 230 336 714 234 339 715 235 341 928 236 342 929 233 338 927 237 343 717 233 340 716 236 344 718 232 337 926 235 341 928 233 338 927 234 339 715 233 340 716 237 343 717 238 345 719 236 344 718 235 346 720 237 343 717 236 344 718 238 345 719 239 347 721 235 346 720 232 348 722 238 345 719 235 346 720 239 347 721 240 349 723 232 348 722 229 350 724 239 347 721 232 348 722 240 349 723 241 351 725 229 350 724 226 352 726 240 349 723 229 350 724 241 351 725 242 353 727 226 352 726 223 354 728 241 351 725 226 352 726 242 353 727 243 355 729 223 354 728 220 356 730 242 353 727 223 354 728 243 355 729 244 357 731 220 356 730 217 358 732 243 355 729 220 356 730 244 357 731 245 359 733 217 358 732 214 360 734 244 357 731 217 358 732 245 359 733 246 361 735 214 360 734 211 362 736 245 359 733 214 360 734 246 361 735 247 363 737 211 362 736 208 364 738 246 361 735 211 362 736 247 363 737 248 365 739 208 364 738 205 366 740 247 363 737 208 364 738 248 365 739 249 367 111 205 366 113 202 368 135 248 365 739 205 366 740 249 367 741 250 369 157 202 368 135 199 370 178 249 367 111 202 368 135 250 369 157 251 371 181 199 370 178 196 372 184 250 369 157 199 370 178 251 371 181 252 373 187 196 372 184 193 374 190 251 371 181 196 372 184 252 373 187 253 375 193 193 374 190 190 376 196 252 373 187 193 374 190 253 375 193 254 377 199 190 376 196 187 378 202 253 375 193 190 376 196 254 377 199 255 379 205 187 378 202 184 380 249 254 377 199 187 378 202 255 379 205 256 381 250 184 380 249 181 382 251 255 379 205 184 380 249 256 381 250 257 383 252 181 382 251 178 384 253 256 381 250 181 382 251 257 383 252 258 385 254 178 384 253 157 386 255 257 383 252 178 384 253 258 385 254 259 387 256 157 386 255 135 388 257 258 385 254 157 386 255 259 387 256 61 91 137 260 389 930 135 201 306 261 390 258 135 388 257 260 391 259 259 387 256 135 388 257 261 390 258 61 91 137 262 392 931 260 389 930 263 393 260 260 391 259 262 394 261 261 390 258 260 391 259 263 393 260 61 91 137 264 395 932 262 392 931 265 396 262 262 394 261 264 397 263 263 393 260 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943 280 419 940 281 420 278 280 421 279 284 424 280 286 427 944 287 428 945 283 423 942 288 429 560 283 425 561 287 430 562 289 431 946 286 427 944 283 423 942 282 422 941 289 431 946 283 423 942 284 424 280 283 425 281 288 429 283 290 432 947 291 433 948 287 428 945 292 434 563 287 430 562 291 435 564 293 436 949 290 432 947 287 428 945 286 427 944 293 436 949 287 428 945 288 429 560 287 430 562 292 434 563 294 437 950 295 438 951 291 433 948 296 439 565 291 435 564 295 440 566 290 432 947 294 437 950 291 433 948 292 434 563 291 435 564 296 439 565 297 441 952 298 442 953 295 438 951 299 443 567 295 440 566 298 444 568 300 445 954 297 441 952 295 438 951 301 446 955 300 445 954 295 438 951 294 437 950 301 446 955 295 438 951 296 439 565 295 440 566 299 443 567 302 447 956 303 448 957 298 442 953 304 449 569 298 444 568 303 450 570 297 441 952 302 447 956 298 442 953 299 443 567 298 444 568 304 449 569 305 451 958 306 452 959 303 448 957 307 453 571 303 450 570 306 454 572 302 447 956 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477 583 324 478 584 327 480 585 328 482 587 326 481 586 323 483 588 327 480 585 326 481 586 328 482 587 329 484 589 323 483 588 320 485 590 328 482 587 323 483 588 329 484 589 330 486 591 320 485 590 317 487 592 329 484 589 320 485 590 330 486 591 331 488 593 317 487 592 314 489 594 330 486 591 317 487 592 331 488 593 332 490 595 314 489 594 311 491 596 331 488 593 314 489 594 332 490 595 333 492 597 311 491 596 308 493 598 332 490 595 311 491 596 333 492 597 334 494 599 308 493 598 305 495 600 333 492 597 308 493 598 334 494 599 335 496 601 305 495 600 302 497 602 334 494 599 305 495 600 335 496 601 336 498 603 302 497 602 297 499 604 335 496 601 302 497 602 336 498 603 337 500 605 297 499 604 300 501 606 336 498 603 297 499 604 337 500 605 338 502 607 300 501 606 301 503 608 337 500 605 300 501 606 338 502 607 339 504 210 301 503 282 294 505 285 338 502 607 301 503 608 339 504 609 340 506 286 294 505 285 290 507 289 339 504 210 294 505 285 340 506 286 341 508 290 290 507 289 293 509 293 340 506 286 290 507 289 341 508 290 342 510 294 293 509 293 286 511 301 341 508 290 293 509 293 342 510 294 343 512 339 286 511 301 289 513 340 342 510 294 286 511 301 343 512 339 344 514 341 289 513 340 282 515 342 343 512 339 289 513 340 344 514 341 345 516 343 282 515 342 285 517 344 344 514 341 282 515 342 345 516 343 346 518 345 285 517 344 61 519 346 345 516 343 285 517 344 346 518 345 347 520 973 348 521 974 61 91 137 349 522 348 61 519 346 348 523 349 350 524 975 347 520 973 61 91 137 351 525 976 350 524 975 61 91 137 352 526 977 351 525 976 61 91 137 353 527 978 352 526 977 61 91 137 354 528 979 353 527 978 61 91 137 355 529 980 354 528 979 61 91 137 356 530 981 355 529 980 61 91 137 357 531 982 356 530 981 61 91 137 358 532 983 357 531 982 61 91 137 0 0 27 358 532 983 61 91 137 346 518 345 61 519 346 349 522 348 359 533 984 360 534 985 348 521 974 361 535 360 348 523 349 360 536 361 347 520 973 359 533 984 348 521 974 349 522 348 348 523 349 361 535 360 362 537 986 363 538 987 360 534 985 364 539 363 360 536 361 363 540 364 359 533 984 362 537 986 360 534 985 361 535 360 360 536 361 364 539 363 362 537 986 365 541 988 363 538 987 366 542 365 363 540 364 365 543 366 364 539 363 363 540 364 366 542 365 367 544 989 368 545 990 365 541 988 369 546 368 365 543 366 368 547 369 362 537 986 367 544 989 365 541 988 366 542 365 365 543 366 369 546 368 367 544 989 370 548 991 368 545 990 371 549 370 368 547 369 370 550 371 369 546 368 368 547 369 371 549 370 372 551 992 373 552 993 370 548 991 374 553 373 370 550 371 373 554 374 367 544 989 372 551 992 370 548 991 371 549 370 370 550 371 374 553 373 372 551 992 375 555 994 373 552 993 376 556 375 373 554 374 375 557 376 374 553 373 373 554 374 376 556 375 377 558 995 378 559 996 375 555 994 379 560 378 375 557 376 378 561 379 372 551 992 377 558 995 375 555 994 376 556 375 375 557 376 379 560 378 380 562 997 381 563 998 378 559 996 382 564 742 378 561 743 381 565 744 377 558 995 380 562 997 378 559 996 379 560 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\n \n \n \n \n \n \n

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0 0 0 1 1 1 2 2 2 3 3 108 2 4 109 1 5 110 4 6 4 0 0 0 2 2 2 5 7 5 4 6 4 2 2 2 6 8 111 5 9 112 2 4 109 6 8 111 2 4 109 3 3 108 7 10 7 8 11 8 1 1 1 9 12 113 1 5 110 8 13 114 0 0 0 7 10 7 1 1 1 9 12 113 3 3 108 1 5 110 10 14 10 11 15 11 8 11 8 12 16 115 8 13 114 11 17 116 7 10 7 10 14 10 8 11 8 9 12 113 8 13 114 12 16 115 13 18 13 14 19 14 11 15 11 15 20 117 11 17 116 14 21 118 10 14 10 13 18 13 11 15 11 12 16 115 11 17 116 15 20 117 16 22 16 17 23 17 14 19 14 18 24 119 14 21 118 17 25 120 13 18 13 16 22 16 14 19 14 15 20 117 14 21 118 18 24 119 19 26 19 20 27 20 17 23 17 21 28 121 17 25 120 20 29 122 16 22 16 19 26 19 17 23 17 18 24 119 17 25 120 21 28 121 22 30 57 23 31 59 20 27 20 24 32 123 20 29 122 23 33 124 19 26 19 22 30 57 20 27 20 21 28 121 20 29 122 24 32 123 25 34 60 26 35 62 23 31 59 27 36 22 23 33 23 26 37 25 22 30 57 25 34 60 23 31 59 24 32 123 23 33 124 27 36 125 28 38 63 29 39 65 26 35 62 30 40 26 26 37 25 29 41 27 25 34 60 28 38 63 26 35 62 27 36 22 26 37 25 30 40 26 31 42 66 32 43 68 29 39 65 33 44 28 29 41 27 32 45 29 28 38 63 31 42 66 29 39 65 30 40 26 29 41 27 33 44 28 34 46 69 35 47 71 32 43 68 36 48 30 32 45 29 35 49 31 31 42 66 34 46 69 32 43 68 33 44 28 32 45 29 36 48 30 37 50 72 38 51 196 35 47 71 39 52 32 35 49 31 38 53 33 34 46 69 37 50 72 35 47 71 36 48 30 35 49 31 39 52 32 40 54 197 41 55 198 38 51 196 42 56 34 38 53 33 41 57 35 37 50 72 40 54 197 38 51 196 39 52 32 38 53 33 42 56 34 43 58 36 41 57 35 40 59 37 42 56 34 41 57 35 43 58 36 44 60 38 40 59 37 37 61 39 43 58 36 40 59 37 44 60 38 45 62 40 37 61 39 34 63 41 44 60 38 37 61 39 45 62 40 46 64 42 34 63 41 31 65 43 45 62 40 34 63 41 46 64 42 47 66 44 31 65 43 28 67 45 46 64 42 31 65 43 47 66 44 48 68 46 28 67 45 25 69 47 47 66 44 28 67 45 48 68 46 49 70 48 25 69 47 22 71 49 48 68 46 25 69 47 49 70 48 50 72 138 22 71 139 19 73 140 49 70 48 22 71 49 50 72 50 51 74 141 19 73 140 16 75 142 50 72 138 19 73 140 51 74 141 52 76 143 16 75 142 13 77 144 51 74 141 16 75 142 52 76 143 53 78 145 13 77 144 10 79 146 52 76 143 13 77 144 53 78 145 54 80 147 10 79 146 7 81 148 53 78 145 10 79 146 54 80 147 55 82 149 7 81 148 0 83 150 54 80 147 7 81 148 55 82 149 56 84 151 0 83 150 4 85 152 55 82 149 0 83 150 56 84 151 5 7 5 57 86 199 4 6 4 58 87 153 4 85 152 57 88 154 56 84 151 4 85 152 58 87 153 59 89 200 60 90 201 57 86 199 61 91 155 57 88 154 60 92 156 5 7 5 59 89 200 57 86 199 58 87 153 57 88 154 61 91 155 62 93 202 63 94 203 60 90 201 64 95 157 60 92 156 63 96 158 59 89 200 62 93 202 60 90 201 61 91 155 60 92 156 64 95 157 65 97 204 66 98 205 63 94 203 67 99 159 63 96 158 66 100 160 62 93 202 65 97 204 63 94 203 64 95 157 63 96 158 67 99 159 68 101 206 69 102 207 66 98 205 70 103 161 66 100 160 69 104 162 65 97 204 68 101 206 66 98 205 67 99 159 66 100 160 70 103 161 71 105 208 72 106 209 69 102 207 73 107 163 69 104 162 72 108 164 68 101 206 71 105 208 69 102 207 70 103 161 69 104 162 73 107 163 74 109 210 75 110 211 72 106 209 76 111 165 72 108 164 75 112 166 71 105 208 74 109 210 72 106 209 73 107 163 72 108 164 76 111 165 77 113 212 78 114 213 75 110 211 79 115 74 75 112 75 78 116 77 74 109 210 77 113 212 75 110 211 76 111 165 75 112 166 79 115 167 80 117 214 81 118 215 78 114 213 82 119 78 78 116 77 81 120 79 77 113 212 80 117 214 78 114 213 79 115 74 78 116 77 82 119 78 83 121 216 84 122 217 81 118 215 85 123 80 81 120 79 84 124 81 80 117 214 83 121 216 81 118 215 82 119 78 81 120 79 85 123 80 86 125 218 87 126 219 84 122 217 88 127 82 84 124 81 87 128 83 83 121 216 86 125 218 84 122 217 85 123 80 84 124 81 88 127 82 89 129 220 90 130 221 87 126 219 91 131 84 87 128 83 90 132 85 86 125 218 89 129 220 87 126 219 88 127 82 87 128 83 91 131 84 92 133 222 93 134 223 90 130 221 94 135 86 90 132 85 93 136 87 89 129 220 92 133 222 90 130 221 91 131 84 90 132 85 94 135 86 95 137 88 93 136 87 92 138 89 94 135 86 93 136 87 95 137 88 96 139 90 92 138 89 89 140 91 95 137 88 92 138 89 96 139 90 97 141 92 89 140 91 86 142 93 96 139 90 89 140 91 97 141 92 98 143 94 86 142 93 83 144 95 97 141 92 86 142 93 98 143 94 99 145 96 83 144 95 80 146 97 98 143 94 83 144 95 99 145 96 100 147 98 80 146 97 77 148 99 99 145 96 80 146 97 100 147 98 101 149 100 77 148 99 74 150 101 100 147 98 77 148 99 101 149 100 102 151 126 74 150 127 71 152 128 101 149 100 74 150 101 102 151 102 103 153 129 71 152 128 68 154 130 102 151 126 71 152 128 103 153 129 104 155 131 68 154 130 65 156 132 103 153 129 68 154 130 104 155 131 105 157 133 65 156 132 62 158 134 104 155 131 65 156 132 105 157 133 106 159 135 62 158 134 59 160 136 105 157 133 62 158 134 106 159 135 107 161 137 59 160 136 5 9 112 106 159 135 59 160 136 107 161 137 107 161 137 5 9 112 6 8 111 61 162 3 6 163 6 3 164 9 58 165 12 61 162 3 3 164 9 9 166 15 58 165 12 3 164 9 64 167 18 107 168 21 6 163 6 61 162 3 64 167 18 6 163 6 67 169 24 106 170 51 107 168 21 64 167 18 67 169 24 107 168 21 70 171 52 105 172 53 106 170 51 67 169 24 70 171 52 106 170 51 73 173 54 104 174 55 105 172 53 70 171 52 73 173 54 105 172 53 76 175 56 103 176 58 104 174 55 73 173 54 76 175 56 104 174 55 79 177 61 102 178 64 103 176 58 76 175 56 79 177 61 103 176 58 82 179 67 101 180 70 102 178 64 79 177 61 82 179 67 102 178 64 85 181 73 100 182 76 101 180 70 82 179 67 85 181 73 101 180 70 88 183 103 99 184 104 100 182 76 85 181 73 88 183 103 100 182 76 91 185 105 98 186 106 99 184 104 88 183 103 91 185 105 99 184 104 94 187 107 97 188 168 98 186 106 91 185 105 94 187 107 98 186 106 95 189 169 96 190 170 97 188 168 94 187 107 95 189 169 97 188 168 9 166 15 56 191 171 58 165 12 12 192 172 55 193 173 56 191 171 9 166 15 12 192 172 56 191 171 15 194 174 54 195 175 55 193 173 12 192 172 15 194 174 55 193 173 18 196 176 53 197 177 54 195 175 15 194 174 18 196 176 54 195 175 21 198 178 52 199 179 53 197 177 18 196 176 21 198 178 53 197 177 24 200 180 51 201 181 52 199 179 21 198 178 24 200 180 52 199 179 27 202 182 50 203 183 51 201 181 24 200 180 27 202 182 51 201 181 30 204 184 49 205 185 50 203 183 27 202 182 30 204 184 50 203 183 33 206 186 48 207 187 49 205 185 30 204 184 33 206 186 49 205 185 36 208 188 47 209 189 48 207 187 33 206 186 36 208 188 48 207 187 39 210 190 46 211 191 47 209 189 36 208 188 39 210 190 47 209 189 42 212 192 45 213 193 46 211 191 39 210 190 42 212 192 46 211 191 43 214 194 44 215 195 45 213 193 42 212 192 43 214 194 45 213 193

\n \n \n \n \n \n \n \n \n 0 0 0\n \n \n \n \n \n \n ./images/3f_pole.jpg\n \n \n \n \n \n \n \n \n 1 0 0 0 0 -4.37114e-8 1 9.31323e-10 0 -1 -4.37114e-8 0 0 0 0 1\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" }, { "path": "turtlebot_description/package.xml", "content": "\n turtlebot_description\n 2.4.2\n \n a revised compact version of official turtlebot_description package for check if collision happened between turtlebot and obstacles in Gazebo by adding libgazebo_ros_bumper.so plugin in kobuki_gazebo.urdf.xacro file. The original file is from turtlebot_description and kobuki_description package.\n \n Liulong Ma\n Liulong Ma\n Liulong Ma\n BSD\n http://ros.org/wiki/turtlebot_description\n https://github.com/turtlebot/turtlebot\n https://github.com/turtlebot/turtlebot/issues\n \n catkin\n\n urdf\n xacro\n\n urdf\n xacro\n kobuki_description\n create_description\n\n" }, { "path": "turtlebot_description/readme.md", "content": "### turtlebot_description\n* To use it you need delete the official turtlebot_description first\n \n sudo apt-get remove ros-kinetic-turtlebot-description\n # cd to workspace\n catkin_make\n \n* Introduction\n\nA revised compact version of official turtlebot_description package to check if collision happened between turtlebot and obstacles in Gazebo by adding libgazebo_ros_bumper.so plugin in [kobuki_gazebo.urdf.xacro](https://github.com/marooncn/navbot/blob/master/turtlebot_description/urdf/kobuki/kobuki_gazebo.urdf.xacro). The original file is from [turtlebot_description](http://wiki.ros.org/turtlebot_description) and [kobuki_description](http://wiki.ros.org/kobuki_description) package.\n\n* Other changes\n\nThe width and height of image captured is 64 and 48 respectively. The size can be change in [turtlebot_gazebo.urdf.xacro](https://github.com/marooncn/navbot/blob/master/turtlebot_description/urdf/turtlebot_gazebo.urdf.xacro).\n" }, { "path": "turtlebot_description/robots/kobuki_hexagons_kinect.urdf.xacro", "content": "\n \n\n\n \n \n \n \n \n \n \n \n\n" }, { "path": "turtlebot_description/urdf/common_properties.urdf.xacro", "content": "\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" }, { "path": "turtlebot_description/urdf/kobuki/common_properties.urdf.xacro", "content": "\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "turtlebot_description/urdf/kobuki/kobuki.urdf.xacro", "content": "\n\n\n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n\n \n\n" }, { "path": "turtlebot_description/urdf/kobuki/kobuki_gazebo.urdf.xacro", "content": "\n\n\n \n\t \n\t 1.0\n\t 1.0\n\t 1000000.0\n\t 100.0\n\t 0.001\n\t 1.0\n\t \n\n\t \n\t 1.0\n\t 1.0\n\t 1000000.0\n\t 100.0\n\t 0.001\n\t 1.0\n\t \n\n\t \n\t 0.0\n\t 0.0\n\t 1000000.0\n\t 100.0\n\t 0.001\n\t 1.0\n\t \n\n\t \n\t 0.0\n\t 0.0\n\t 1000000.0\n\t 100.0\n\t 0.001\n\t 1.0\n\t \n\n\t \n\t 0.3\n\t 0.3\n\t \n\t true\n\t 50.0\n\t true\n\t \n\t base_footprint_fixed_joint_lump__base_collision\n\t \n \n contact_state\n \n\t \n\n\t \n\n\t \n\t \n\t true\n\t 50\n\t true\n\t \n\t \n\t \n\t 50\n\t 1.0\n\t -0.0436 \n\t 0.0436 \n\t \n\n\n\n\n\n\n\n\n\t \n\t \n\t 0.01\n\t 0.15\n\t 1.0\n\t \n\t \n\t \n\t \n\n\t \n\t \n\t true\n\t 50\n\t true\n\t \n\t \n\t \n\t 50\n\t 1.0\n\t -0.0436 \n\t 0.0436 \n\t \n\n\n\n\n\n\n\n\n\t \n\t \n\t 0.01\n\t 0.15\n\t 1.0\n\t \n\t \n\t \n\t \n\n\t \n\t \n\t true\n\t 50\n\t true\n\t \n\t \n\t \n\t 50\n\t 1.0\n\t -0.0436 \n\t 0.0436 \n\t \n\n\n\n\n\n\n\n\n\t \n\t \n\t 0.01\n\t 0.15\n\t 1.0\n\t \n\t \n\t \n\t \n\n\t \n\t \n true\n 50\n false\n \n \n gaussian\n\t \n\t 0.0\n\t ${0.0014*0.0014} \n\t 0.0\n\t 0.0\n\t \n\t\t \n\t\t\t 0.0\n\t\t\t 1.7e-2\n\t\t\t 0.1\n\t\t\t 0.001\n\t\t \n \n\t \n \n\t \n\n\t \n\t \n\t 1\n\t wheel_left_joint\n\t wheel_right_joint\n\t .230\n\t 0.070\n\t 1.0\n\t 0.6\n\t cliff_sensor_left\n\t cliff_sensor_front\n\t cliff_sensor_right\n\t 0.04\n\t bumpers\n imu\n\t \n\t \n \n\n\n" }, { "path": "turtlebot_description/urdf/sensors/kinect.urdf.xacro", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \t \n \n \t \n \t \n \t \n \t \n \n \n \n \n \n \n\n \n\t\n\t \n\t \n\t \n\t\n\t\n\n\t\n\t \n\t \n\t \n\t\n\t\n\t\n\t\n\t\n \n\n" }, { "path": "turtlebot_description/urdf/stacks/hexagons.urdf.xacro", "content": "\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "turtlebot_description/urdf/turtlebot_common_library.urdf.xacro", "content": "\n\n\n \n \n \n\n" }, { "path": "turtlebot_description/urdf/turtlebot_gazebo.urdf.xacro", "content": "\n\n \n \n \n \n true\n 20.0\n \n ${60.0*M_PI/180.0}\n \n B8G8R8\n 64 \n 48 \n \n \n 0.05\n 8.0\n \n \n \n camera\n true\n 10\n rgb/image_raw\n depth/image_raw\n depth/points\n rgb/camera_info\n depth/camera_info\n camera_depth_optical_frame\n 0.1\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.4\n \n \n \n \n\n" }, { "path": "turtlebot_description/urdf/turtlebot_properties.urdf.xacro", "content": "\n\n \n \n \n\t\n\n\t\n\t\n\t\n\t\n\n" } ]