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Repository: PWhiddy/PokemonRedExperiments
Branch: master
Commit: 7739b399db50
Files: 59
Total size: 27.8 MB
Directory structure:
gitextract_qv5_0ma5/
├── .gitignore
├── LICENSE
├── README.md
├── VisualizeProgress.ipynb
├── baselines/
│ ├── Visualizations_over_time.ipynb
│ ├── agent_enabled.txt
│ ├── baseline_fast_minimal.py
│ ├── delete_empty_imgs.txt
│ ├── events.json
│ ├── global_map.py
│ ├── grid_renders/
│ │ └── parallel_scripts/
│ │ └── parallel_grid_convert.sh
│ ├── map_data.json
│ ├── memory_addresses.py
│ ├── ray_exp/
│ │ ├── red_gym_env_ray.py
│ │ ├── requirements.txt
│ │ └── train_ray.py
│ ├── red_gym_env.py
│ ├── red_gym_env_minimal.py
│ ├── render_all_needed_grids.py
│ ├── render_all_needed_grids.sh
│ ├── requirements-unfrozen.txt
│ ├── requirements.txt
│ ├── run_baseline_parallel.py
│ ├── run_baseline_parallel_fast.py
│ ├── run_pretrained_interactive.py
│ ├── run_recorded_actions.py
│ ├── saves_to_record.txt
│ ├── stream_agent_wrapper.py
│ ├── tensorboard_callback.py
│ └── tile_vids_to_grid.py
├── clip_experiment/
│ └── Interacting_with_CLIP_Pokemon.ipynb
├── fast_text_start.state
├── has_pokedex.state
├── has_pokedex_nballs.state
├── init.state
├── v2/
│ ├── agent_enabled.txt
│ ├── baseline_fast_v2.py
│ ├── events.json
│ ├── global_map.py
│ ├── go_forever.sh
│ ├── macos_requirements.txt
│ ├── map_data.json
│ ├── red_gym_env_v2.py
│ ├── requirements.txt
│ ├── run_pretrained_interactive.py
│ ├── runs/
│ │ └── .gitignore
│ ├── stream_agent_wrapper.py
│ └── tensorboard_callback.py
├── visualization/
│ ├── Agent_Visualization.ipynb
│ ├── BetterMapVis.ipynb
│ ├── BetterMapVis_script_version.py
│ ├── BetterMapVis_script_version_FLOW.py
│ ├── BetterMapVis_script_version_FLOW_edge.py
│ ├── BetterMapVis_script_version_PROG_COLOR.py
│ ├── Create_Video_Grids.ipynb
│ ├── MapWalkingVis.ipynb
│ ├── Map_Stitching.ipynb
│ └── poke_map/
│ └── pokemap_full_rough.psd
└── windows-setup-guide.md
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
__pycache__
.DS_Store
rollouts
*.gb
*.ram
.ipynb_checkpoints
raw_documentation
*.pem
_autoplay.mp4
session_*
.env
PokemonRed.gb.state
venv*
.python-version
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2023 Peter Whidden
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
# Train RL agents to play Pokemon Red
### New 10-19-24! Updated & Simplified V2 Training Script - See V2 below
### New 1-29-24! - [Multiplayer Live Training Broadcast](https://github.com/pwhiddy/pokerl-map-viz/) 🎦 🔴 [View Here](https://pwhiddy.github.io/pokerl-map-viz/)
Stream your training session to a shared global game map using the [Broadcast Wrapper](/baselines/stream_agent_wrapper.py)
See how in [Training Broadcast](#training-broadcast) section
## Watch the Video on Youtube!
<p float="left">
<a href="https://youtu.be/DcYLT37ImBY">
<img src="/assets/youtube.jpg?raw=true" height="192">
</a>
<a href="https://youtu.be/DcYLT37ImBY">
<img src="/assets/poke_map.gif?raw=true" height="192">
</a>
</p>
## Join the discord server
[](http://discord.gg/RvadteZk4G)
## Running the Pretrained Model Interactively 🎮
🐍 Python 3.10+ is recommended. Other versions may work but have not been tested.
You also need to install ffmpeg and have it available in the command line.
### Windows Setup
Refer to this [Windows Setup Guide](windows-setup-guide.md)
### For AMD GPUs
Follow this [guide to install pytorch with ROCm support](https://rocm.docs.amd.com/projects/radeon/en/latest/docs/install/wsl/howto_wsl.html)
### Linux / MacOS
V2 is now recommended over the original version. You may follow all steps below but replace `baselines` with `v2`.
1. Copy your legally obtained Pokemon Red ROM into the base directory. You can find this using google, it should be 1MB. Rename it to `PokemonRed.gb` if it is not already. The sha1 sum should be `ea9bcae617fdf159b045185467ae58b2e4a48b9a`, which you can verify by running `shasum PokemonRed.gb`.
2. Move into the `baselines/` directory:
```cd baselines```
3. Install dependencies:
```pip install -r requirements.txt```
It may be necessary in some cases to separately install the SDL libraries.
For V2 MacOS users should use ```macos_requirements.txt``` instead of ```requirements.txt```
4. Run:
```python run_pretrained_interactive.py```
Interact with the emulator using the arrow keys and the `a` and `s` keys (A and B buttons).
You can pause the AI's input during the game by editing `agent_enabled.txt`
Note: the Pokemon.gb file MUST be in the main directory and your current directory MUST be the `baselines/` directory in order for this to work.
## Training the Model 🏋️
<img src="/assets/grid.png?raw=true" height="156">
### V2
- Trains faster and with less memory
- Reaches Cerulean
- Streams to map by default
- Other improvements
Replaces the frame KNN with a coordinate based exploration reward, as well as some other tweaks.
1. Previous steps but in the `v2` directory instead of `baselines`
2. Run:
```python baseline_fast_v2.py```
## Tracking Training Progress 📈
### Training Broadcast
Stream your training session to a shared global game map using the [Broadcast Wrapper](/baselines/stream_agent_wrapper.py) on your environment like this:
```python
env = StreamWrapper(
env,
stream_metadata = { # All of this is part is optional
"user": "super-cool-user", # choose your own username
"env_id": id, # environment identifier
"color": "#0033ff", # choose your color :)
"extra": "", # any extra text you put here will be displayed
}
)
```
Hack on the broadcast viewing client or set up your own local stream with this repo:
https://github.com/pwhiddy/pokerl-map-viz/
### Local Metrics
The current state of each game is rendered to images in the session directory.
You can track the progress in tensorboard by moving into the session directory and running:
```tensorboard --logdir .```
You can then navigate to `localhost:6006` in your browser to view metrics.
To enable wandb integration, change `use_wandb_logging` in the training script to `True`.
## Static Visualization 🐜
Map visualization code can be found in `visualization/` directory.
## Follow up work
Check out our follow up projects & papers!
### [Pokemon Red via Reinforcement Learning 🔗](https://arxiv.org/abs/2502.19920)
```
@misc{pleines2025pokemon,
title={Pokemon Red via Reinforcement Learning},
author={Marco Pleines and Daniel Addis and David Rubinstein and Frank Zimmer and Mike Preuss and Peter Whidden},
year={2025},
eprint={2502.19920},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
### [Pokemon RL Edition 🔗](https://drubinstein.github.io/pokerl/)
### [PokeGym 🔗](https://github.com/PufferAI/pokegym)
## Supporting Libraries
Check out these awesome projects!
### [PyBoy](https://github.com/Baekalfen/PyBoy)
<a href="https://github.com/Baekalfen/PyBoy">
<img src="/assets/pyboy.svg" height="64">
</a>
### [Stable Baselines 3](https://github.com/DLR-RM/stable-baselines3)
<a href="https://github.com/DLR-RM/stable-baselines3">
<img src="/assets/sblogo.png" height="64">
</a>
================================================
FILE: VisualizeProgress.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 218,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.cm as mplcm\n",
"import matplotlib.colors as colors\n",
"from einops import rearrange\n",
"from PIL import Image\n",
"f tqdm\n",
"import json\n",
"import time\n",
"from pathlib import Path\n",
"from sklearn import linear_model\n",
"import mediapy as media"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"total 1712\r\n",
"-rw-r--r-- 1 peterwhidden staff 817492 Jun 4 16:16 Visualizations_over_time.ipynb\r\n",
"drwxr-xr-x 485 peterwhidden staff 15520 Jun 4 03:04 \u001b[34msession_e41c9eff_second_maybe\u001b[m\u001b[m\r\n",
"-rw-r--r-- 1 peterwhidden staff 2856 Apr 29 23:53 run_pretrained_interactive.py\r\n",
"-rw-r--r-- 1 peterwhidden staff 2669 Apr 29 23:52 run_baseline_parallel.py\r\n",
"-rw-r--r-- 1 peterwhidden staff 51 Apr 29 23:51 requirements.txt\r\n",
"drwxr-xr-x 4 peterwhidden staff 128 Apr 29 23:46 \u001b[34msession_96d6fd08\u001b[m\u001b[m\r\n",
"drwxr-xr-x 2 root staff 64 Apr 29 23:44 \u001b[34msession_8630f89b\u001b[m\u001b[m\r\n",
"drwxr-xr-x 2 root staff 64 Apr 29 20:59 \u001b[34msession_62bce973\u001b[m\u001b[m\r\n",
"drwxr-xr-x 2 root staff 64 Apr 29 20:56 \u001b[34msession_94adeb2f\u001b[m\u001b[m\r\n",
"drwxr-xr-x 2 root staff 64 Apr 29 20:54 \u001b[34msession_4d0af04c\u001b[m\u001b[m\r\n",
"drwxr-xr-x 2 peterwhidden staff 64 Apr 29 20:53 \u001b[34msession_dcb93b1a\u001b[m\u001b[m\r\n",
"drwxr-xr-x 3 peterwhidden staff 96 Apr 29 20:48 \u001b[34msession_2f7889c3\u001b[m\u001b[m\r\n",
"drwxr-xr-x 4 peterwhidden staff 128 Apr 29 18:27 \u001b[34msession_a5c72e1f\u001b[m\u001b[m\r\n",
"drwxr-xr-x 6 peterwhidden staff 192 Apr 29 18:25 \u001b[34msession_d905264b\u001b[m\u001b[m\r\n",
"drwxr-xr-x 4 peterwhidden staff 128 Apr 29 18:22 \u001b[34msession_5340a529\u001b[m\u001b[m\r\n",
"drwxr-xr-x 8 peterwhidden staff 256 Apr 29 18:17 \u001b[34msession_e7b2d2f0\u001b[m\u001b[m\r\n",
"drwxr-xr-x 4 peterwhidden staff 128 Apr 29 17:54 \u001b[34msession_ba5cb0c3\u001b[m\u001b[m\r\n",
"drwxr-xr-x 4 peterwhidden staff 128 Apr 29 17:34 \u001b[34msession_f1bc8558\u001b[m\u001b[m\r\n",
"drwxr-xr-x 4 peterwhidden staff 128 Apr 29 17:18 \u001b[34msession_320f5de6\u001b[m\u001b[m\r\n",
"drwxr-xr-x 2 peterwhidden staff 64 Apr 29 17:14 \u001b[34msession_f09d6460\u001b[m\u001b[m\r\n",
"drwxr-xr-x 3 peterwhidden staff 96 Apr 29 17:12 \u001b[34msession_7432809d\u001b[m\u001b[m\r\n",
"drwxr-xr-x 3 peterwhidden staff 96 Apr 29 17:12 \u001b[34m__pycache__\u001b[m\u001b[m\r\n",
"-rw-r--r-- 1 peterwhidden staff 3073 Apr 23 21:48 tile_vids_to_grid.py\r\n",
"drwxr-xr-x 3 peterwhidden staff 96 Apr 23 21:48 \u001b[34msession_e1b6d2dc_post_train_more_steps\u001b[m\u001b[m\r\n",
"drwxr-xr-x 4 peterwhidden staff 128 Apr 23 21:48 \u001b[34msession_bfdca25a\u001b[m\u001b[m\r\n",
"drwxr-xr-x 3 peterwhidden staff 96 Apr 23 21:48 \u001b[34msession_b30478f4\u001b[m\u001b[m\r\n",
"drwxr-xr-x 3 peterwhidden staff 96 Apr 23 21:48 \u001b[34msession_4da05e87_main_good\u001b[m\u001b[m\r\n",
"-rw-r--r-- 1 peterwhidden staff 2580 Apr 23 21:48 saves_to_record.txt\r\n",
"-rw-r--r-- 1 peterwhidden staff 1541 Apr 23 21:48 run_baseline.py\r\n",
"-rwxr-xr-x 1 peterwhidden staff 83 Apr 23 21:48 \u001b[31mrender_all_needed_grids.sh\u001b[m\u001b[m\r\n",
"-rw-r--r-- 1 peterwhidden staff 2891 Apr 23 21:48 render_all_needed_grids.py\r\n",
"-rw-r--r-- 1 peterwhidden staff 19972 Apr 23 21:48 red_gym_env.py\r\n",
"drwxr-xr-x 8 peterwhidden staff 256 Apr 23 21:48 \u001b[34mgrinds_to_level_11_extra_time_14\u001b[m\u001b[m\r\n",
"drwxr-xr-x 3 peterwhidden staff 96 Apr 23 21:48 \u001b[34mgrid_renders\u001b[m\u001b[m\r\n",
"-rw-r--r-- 1 peterwhidden staff 53 Apr 23 21:48 delete_empty_imgs.txt\r\n",
"drwxr-xr-x 3 peterwhidden staff 96 Apr 23 21:48 \u001b[34mbest_12-7\u001b[m\u001b[m\r\n",
"drwxr-xr-x 746 peterwhidden staff 23872 Feb 5 12:19 \u001b[34msession_4da05e87_main_full\u001b[m\u001b[m\r\n"
]
}
],
"source": [
"!ls -lt baselines"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def load_run_data(run_path):\n",
" run_lists = []\n",
" for p in Path(run_path).glob('all_runs_*.json'):\n",
" with open(p, 'r') as f:\n",
" run_lists.append(json.load(f))\n",
" all_runs = [val for tup in zip(*run_lists) for val in tup]\n",
" print(f'runs loaded: {len(all_runs)}')\n",
" return all_runs"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def plot_runs(\n",
" all_runs, use_keys=None, \n",
" plot_fit=False, plot_line=False, \n",
" start=0, group_runs=1,\n",
" agg_func=np.mean):\n",
" \n",
" all_runs = all_runs[start:]\n",
" x = np.arange(len(all_runs)//group_runs)\n",
" \n",
" def add_plot(y, name, alpha, size, plot_line, plot_fit):\n",
" plt.scatter(x, y, alpha=alpha, s=size, label=name)\n",
" if plot_line:\n",
" plt.plot(x, y, alpha=alpha, label=name)\n",
" if plot_fit:\n",
" regr = linear_model.LinearRegression()\n",
" # Optimize linear model\n",
" regr.fit(x.reshape(-1, 1), y.reshape(-1, 1))\n",
"\n",
" # Make predictions using the testing set\n",
" lin_y = regr.predict(x.reshape(-1, 1))\n",
" plt.plot(x, lin_y, linewidth=3)\n",
" \n",
" # convert list of dictionaries to dictionary of lists\n",
" metrics = {m: [run[m] for run in all_runs] for m in all_runs[0].keys()}\n",
" metrics['total'] = [sum([v for _,v in run.items()]) for run in all_runs]\n",
" if group_runs:\n",
" for m, dat in metrics.items():\n",
" metrics[m] = agg_func(np.array(dat).reshape(-1, group_runs), 1)\n",
" available_keys = metrics.keys()\n",
" print(f'available metrics: {list(available_keys)}')\n",
" if use_keys is None:\n",
" use_keys = available_keys\n",
" \n",
" ## TODO subplot for each metric ## \n",
" NUM_COLORS = len(use_keys)\n",
" cm = plt.get_cmap('Dark2')\n",
" cNorm = colors.Normalize(vmin=0, vmax=NUM_COLORS-1)\n",
" scalarMap = mplcm.ScalarMappable(norm=cNorm, cmap=cm)\n",
" fig = plt.figure(figsize = (12, 8))\n",
"\n",
" ax = fig.add_subplot(111)\n",
" ax.set_prop_cycle(color=[scalarMap.to_rgba(i) for i in range(NUM_COLORS)])\n",
"\n",
" for i, m in enumerate(use_keys):\n",
" add_plot(\n",
" np.array(metrics[m]), m, 1.0, 3.0, plot_line, plot_fit\n",
" )\n",
"\n",
" plt.title('Reward over runs PPO')\n",
" plt.legend(bbox_to_anchor=(1.04, 0.5), loc=\"center left\", borderaxespad=0)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def get_latest_grid(pth):\n",
" imgs = np.array([np.array(Image.open(p)) for p in Path(pth).glob('curframe*.jpeg')])\n",
" grid = rearrange(imgs, '(h2 w2) h w c -> (h2 h) (w2 w) c', w2=4)\n",
" return grid"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"\\nget_im_func = lambda: get_latest_grid('baselines/session_d34118d3')\\n\\nwith media.VideoWriter('test_grid.mp4', get_im_func().shape[:2]) as wr:\\n for i in range(500):\\n #wr.add_image(get_im_func())\\n #time.sleep(1)\\n\""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''\n",
"get_im_func = lambda: get_latest_grid('baselines/session_d34118d3')\n",
"\n",
"with media.VideoWriter('test_grid.mp4', get_im_func().shape[:2]) as wr:\n",
" for i in range(500):\n",
" #wr.add_image(get_im_func())\n",
" #time.sleep(1)\n",
"''' "
]
},
{
"cell_type": "code",
"execution_count": 231,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f5e63047e10>"
]
},
"execution_count": 231,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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
gitextract_qv5_0ma5/ ├── .gitignore ├── LICENSE ├── README.md ├── VisualizeProgress.ipynb ├── baselines/ │ ├── Visualizations_over_time.ipynb │ ├── agent_enabled.txt │ ├── baseline_fast_minimal.py │ ├── delete_empty_imgs.txt │ ├── events.json │ ├── global_map.py │ ├── grid_renders/ │ │ └── parallel_scripts/ │ │ └── parallel_grid_convert.sh │ ├── map_data.json │ ├── memory_addresses.py │ ├── ray_exp/ │ │ ├── red_gym_env_ray.py │ │ ├── requirements.txt │ │ └── train_ray.py │ ├── red_gym_env.py │ ├── red_gym_env_minimal.py │ ├── render_all_needed_grids.py │ ├── render_all_needed_grids.sh │ ├── requirements-unfrozen.txt │ ├── requirements.txt │ ├── run_baseline_parallel.py │ ├── run_baseline_parallel_fast.py │ ├── run_pretrained_interactive.py │ ├── run_recorded_actions.py │ ├── saves_to_record.txt │ ├── stream_agent_wrapper.py │ ├── tensorboard_callback.py │ └── tile_vids_to_grid.py ├── clip_experiment/ │ └── Interacting_with_CLIP_Pokemon.ipynb ├── fast_text_start.state ├── has_pokedex.state ├── has_pokedex_nballs.state ├── init.state ├── v2/ │ ├── agent_enabled.txt │ ├── baseline_fast_v2.py │ ├── events.json │ ├── global_map.py │ ├── go_forever.sh │ ├── macos_requirements.txt │ ├── map_data.json │ ├── red_gym_env_v2.py │ ├── requirements.txt │ ├── run_pretrained_interactive.py │ ├── runs/ │ │ └── .gitignore │ ├── stream_agent_wrapper.py │ └── tensorboard_callback.py ├── visualization/ │ ├── Agent_Visualization.ipynb │ ├── BetterMapVis.ipynb │ ├── BetterMapVis_script_version.py │ ├── BetterMapVis_script_version_FLOW.py │ ├── BetterMapVis_script_version_FLOW_edge.py │ ├── BetterMapVis_script_version_PROG_COLOR.py │ ├── Create_Video_Grids.ipynb │ ├── MapWalkingVis.ipynb │ ├── Map_Stitching.ipynb │ └── poke_map/ │ └── pokemap_full_rough.psd └── windows-setup-guide.md
SYMBOL INDEX (218 symbols across 23 files)
FILE: baselines/baseline_fast_minimal.py
function make_env (line 15) | def make_env(rank, seed=0):
FILE: baselines/global_map.py
function local_to_global (line 17) | def local_to_global(r: int, c: int, map_n: int):
FILE: baselines/ray_exp/red_gym_env_ray.py
class RedGymEnv (line 27) | class RedGymEnv(gym.Env):
method __init__ (line 30) | def __init__(
method reset (line 112) | def reset(self, *, seed=None, options=None):
method init_knn (line 152) | def init_knn(self):
method render (line 159) | def render(self, reduce_res=True, add_memory=True, update_mem=True):
method step (line 180) | def step(self, action):
method run_action_on_emulator (line 216) | def run_action_on_emulator(self, action):
method add_video_frame (line 236) | def add_video_frame(self):
method append_agent_stats (line 240) | def append_agent_stats(self):
method update_frame_knn_index (line 254) | def update_frame_knn_index(self, frame_vec):
method update_reward (line 274) | def update_reward(self):
method group_rewards (line 292) | def group_rewards(self):
method create_exploration_memory (line 299) | def create_exploration_memory(self):
method create_recent_memory (line 328) | def create_recent_memory(self):
method check_if_done (line 334) | def check_if_done(self):
method save_and_print_info (line 344) | def save_and_print_info(self, done, obs_memory):
method read_m (line 380) | def read_m(self, addr):
method read_bit (line 383) | def read_bit(self, addr, bit: int) -> bool:
method get_levels_sum (line 387) | def get_levels_sum(self):
method get_levels_reward (line 391) | def get_levels_reward(self):
method get_knn_reward (line 402) | def get_knn_reward(self):
method get_badges (line 410) | def get_badges(self):
method read_party (line 413) | def read_party(self):
method update_heal_reward (line 416) | def update_heal_reward(self):
method get_all_events_reward (line 428) | def get_all_events_reward(self):
method get_game_state_reward (line 431) | def get_game_state_reward(self, print_stats=False):
method save_screenshot (line 472) | def save_screenshot(self, name):
method update_max_op_level (line 479) | def update_max_op_level(self):
method update_max_event_rew (line 487) | def update_max_event_rew(self):
method read_hp_fraction (line 492) | def read_hp_fraction(self):
method read_hp (line 497) | def read_hp(self, start):
method bit_count (line 501) | def bit_count(self, bits):
method read_triple (line 504) | def read_triple(self, start_add):
method read_bcd (line 507) | def read_bcd(self, num):
method read_money (line 510) | def read_money(self):
FILE: baselines/red_gym_env.py
class RedGymEnv (line 23) | class RedGymEnv(Env):
method __init__ (line 26) | def __init__(
method reset (line 118) | def reset(self, seed=None, options=None):
method init_knn (line 163) | def init_knn(self):
method init_map_mem (line 170) | def init_map_mem(self):
method render (line 173) | def render(self, reduce_res=True, add_memory=True, update_mem=True):
method step (line 194) | def step(self, action):
method run_action_on_emulator (line 233) | def run_action_on_emulator(self, action):
method add_video_frame (line 258) | def add_video_frame(self):
method append_agent_stats (line 262) | def append_agent_stats(self, action):
method update_frame_knn_index (line 285) | def update_frame_knn_index(self, frame_vec):
method update_seen_coords (line 306) | def update_seen_coords(self):
method update_reward (line 318) | def update_reward(self):
method group_rewards (line 336) | def group_rewards(self):
method create_exploration_memory (line 346) | def create_exploration_memory(self):
method create_recent_memory (line 379) | def create_recent_memory(self):
method check_if_done (line 385) | def check_if_done(self):
method save_and_print_info (line 395) | def save_and_print_info(self, done, obs_memory):
method read_m (line 431) | def read_m(self, addr):
method read_bit (line 434) | def read_bit(self, addr, bit: int) -> bool:
method get_levels_sum (line 438) | def get_levels_sum(self):
method get_levels_reward (line 442) | def get_levels_reward(self):
method get_knn_reward (line 453) | def get_knn_reward(self):
method get_badges (line 462) | def get_badges(self):
method read_party (line 465) | def read_party(self):
method update_heal_reward (line 468) | def update_heal_reward(self):
method get_all_events_reward (line 482) | def get_all_events_reward(self):
method get_game_state_reward (line 500) | def get_game_state_reward(self, print_stats=False):
method save_screenshot (line 541) | def save_screenshot(self, name):
method update_max_op_level (line 548) | def update_max_op_level(self):
method update_max_event_rew (line 556) | def update_max_event_rew(self):
method read_hp_fraction (line 561) | def read_hp_fraction(self):
method read_hp (line 567) | def read_hp(self, start):
method bit_count (line 571) | def bit_count(self, bits):
method read_triple (line 574) | def read_triple(self, start_add):
method read_bcd (line 577) | def read_bcd(self, num):
method read_money (line 580) | def read_money(self):
method get_map_location (line 585) | def get_map_location(self, map_idx):
FILE: baselines/red_gym_env_minimal.py
class PokeRedEnv (line 20) | class PokeRedEnv(Env):
method __init__ (line 21) | def __init__(
method reset (line 92) | def reset(self, seed=0, options={}):
method init_map_mem (line 124) | def init_map_mem(self):
method render (line 127) | def render(self, reduce_res=True):
method _get_obs (line 136) | def _get_obs(self):
method step (line 146) | def step(self, action):
method run_action_on_emulator (line 197) | def run_action_on_emulator(self, action):
method append_agent_stats (line 213) | def append_agent_stats(self, action):
method get_game_coords (line 240) | def get_game_coords(self):
method update_seen_coords (line 243) | def update_seen_coords(self):
method get_global_coords (line 248) | def get_global_coords(self):
method update_explore_map (line 255) | def update_explore_map(self):
method get_explore_map (line 262) | def get_explore_map(self):
method check_if_done (line 273) | def check_if_done(self):
method read_m (line 278) | def read_m(self, addr):
method read_bit (line 281) | def read_bit(self, addr, bit: int) -> bool:
method read_event_bits (line 285) | def read_event_bits(self):
method get_levels_sum (line 291) | def get_levels_sum(self):
method get_badges (line 300) | def get_badges(self):
method read_party (line 303) | def read_party(self):
method get_all_events_reward (line 309) | def get_all_events_reward(self):
method update_max_op_level (line 321) | def update_max_op_level(self):
method update_heal_reward (line 333) | def update_heal_reward(self):
method read_hp_fraction (line 343) | def read_hp_fraction(self):
method read_hp (line 355) | def read_hp(self, start):
method bit_count (line 359) | def bit_count(self, bits):
method update_map_progress (line 362) | def update_map_progress(self):
method get_map_progress (line 366) | def get_map_progress(self, map_idx):
method get_map_location (line 372) | def get_map_location(self, map_idx):
FILE: baselines/render_all_needed_grids.py
function make_env (line 12) | def make_env(rank, env_conf, seed=0):
function run_save (line 27) | def run_save(save):
FILE: baselines/run_baseline_parallel.py
function make_env (line 11) | def make_env(rank, env_conf, seed=0):
FILE: baselines/run_baseline_parallel_fast.py
function make_env (line 12) | def make_env(rank, env_conf, seed=0):
FILE: baselines/run_pretrained_interactive.py
function make_env (line 11) | def make_env(rank, env_conf, seed=0):
FILE: baselines/run_recorded_actions.py
function run_recorded_actions_on_emulator_and_save_video (line 6) | def run_recorded_actions_on_emulator_and_save_video(sess_id, instance_id...
FILE: baselines/stream_agent_wrapper.py
class StreamWrapper (line 10) | class StreamWrapper(gym.Wrapper):
method __init__ (line 11) | def __init__(self, env, stream_metadata={}):
method step (line 32) | def step(self, action):
method broadcast_ws_message (line 58) | async def broadcast_ws_message(self, message):
method establish_wc_connection (line 67) | async def establish_wc_connection(self):
FILE: baselines/tensorboard_callback.py
function merge_dicts (line 10) | def merge_dicts(dicts):
class TensorboardCallback (line 29) | class TensorboardCallback(BaseCallback):
method __init__ (line 31) | def __init__(self, log_dir, verbose=0):
method _on_training_start (line 36) | def _on_training_start(self):
method _on_step (line 40) | def _on_step(self) -> bool:
method _on_training_end (line 71) | def _on_training_end(self):
FILE: baselines/tile_vids_to_grid.py
function run_ffmpeg_grid (line 9) | def run_ffmpeg_grid(out_path, files, screen_res_str, full_res_string, gx...
function make_script (line 60) | def make_script(path):
function make_outer_script (line 69) | def make_outer_script(out_file, paths):
function write_file (line 74) | def write_file(out_file, script):
FILE: v2/baseline_fast_v2.py
function make_env (line 13) | def make_env(rank, env_conf, seed=0):
FILE: v2/global_map.py
function local_to_global (line 17) | def local_to_global(r: int, c: int, map_n: int):
FILE: v2/red_gym_env_v2.py
class RedGymEnv (line 22) | class RedGymEnv(Env):
method __init__ (line 23) | def __init__(self, config=None):
method reset (line 123) | def reset(self, seed=None, options={}):
method init_map_mem (line 167) | def init_map_mem(self):
method render (line 170) | def render(self, reduce_res=True):
method _get_obs (line 178) | def _get_obs(self):
method step (line 201) | def step(self, action):
method run_action_on_emulator (line 249) | def run_action_on_emulator(self, action):
method append_agent_stats (line 262) | def append_agent_stats(self, action):
method start_video (line 288) | def start_video(self):
method add_video_frame (line 323) | def add_video_frame(self):
method get_game_coords (line 334) | def get_game_coords(self):
method update_seen_coords (line 337) | def update_seen_coords(self):
method get_current_coord_count_reward (line 348) | def get_current_coord_count_reward(self):
method get_global_coords (line 357) | def get_global_coords(self):
method update_explore_map (line 361) | def update_explore_map(self):
method get_explore_map (line 369) | def get_explore_map(self):
method update_recent_screens (line 380) | def update_recent_screens(self, cur_screen):
method update_recent_actions (line 384) | def update_recent_actions(self, action):
method update_reward (line 388) | def update_reward(self):
method group_rewards (line 399) | def group_rewards(self):
method check_if_done (line 408) | def check_if_done(self):
method save_and_print_info (line 413) | def save_and_print_info(self, done, obs):
method read_m (line 459) | def read_m(self, addr):
method read_bit (line 463) | def read_bit(self, addr, bit: int) -> bool:
method read_event_bits (line 467) | def read_event_bits(self):
method get_levels_sum (line 473) | def get_levels_sum(self):
method get_levels_reward (line 482) | def get_levels_reward(self):
method get_badges (line 493) | def get_badges(self):
method read_party (line 496) | def read_party(self):
method get_all_events_reward (line 502) | def get_all_events_reward(self):
method get_game_state_reward (line 514) | def get_game_state_reward(self, print_stats=False):
method update_max_op_level (line 530) | def update_max_op_level(self):
method update_max_event_rew (line 542) | def update_max_event_rew(self):
method update_heal_reward (line 547) | def update_heal_reward(self):
method read_hp_fraction (line 557) | def read_hp_fraction(self):
method read_hp (line 569) | def read_hp(self, start):
method bit_count (line 573) | def bit_count(self, bits):
method fourier_encode (line 576) | def fourier_encode(self, val):
method update_map_progress (line 579) | def update_map_progress(self):
method get_map_progress (line 583) | def get_map_progress(self, map_idx):
FILE: v2/run_pretrained_interactive.py
function make_env (line 14) | def make_env(rank, env_conf, seed=0):
function get_most_recent_zip_with_age (line 29) | def get_most_recent_zip_with_age(folder_path):
FILE: v2/stream_agent_wrapper.py
class StreamWrapper (line 10) | class StreamWrapper(gym.Wrapper):
method __init__ (line 11) | def __init__(self, env, stream_metadata={}):
method step (line 32) | def step(self, action):
method broadcast_ws_message (line 58) | async def broadcast_ws_message(self, message):
method establish_wc_connection (line 67) | async def establish_wc_connection(self):
FILE: v2/tensorboard_callback.py
function merge_dicts (line 10) | def merge_dicts(dicts):
class TensorboardCallback (line 29) | class TensorboardCallback(BaseCallback):
method __init__ (line 31) | def __init__(self, log_dir, verbose=0):
method _on_training_start (line 36) | def _on_training_start(self):
method _on_step (line 40) | def _on_step(self) -> bool:
method _on_training_end (line 71) | def _on_training_end(self):
FILE: visualization/BetterMapVis_script_version.py
function make_all_coords_arrays (line 15) | def make_all_coords_arrays(filtered_dfs):
function load_tex (line 18) | def load_tex(name):
function get_sprite_by_coords (line 22) | def get_sprite_by_coords(img, x, y):
function game_coord_to_pixel_coord (line 29) | def game_coord_to_pixel_coord(
function add_sprite (line 77) | def add_sprite(overlay_map, sprite, coord):
function blend_overlay (line 91) | def blend_overlay(background, over):
function split (line 97) | def split(img):
function render_video (line 100) | def render_video(fname, all_coords, walks, bg, inter_steps=4, add_start=...
function test_render (line 171) | def test_render(name, dat, walks, bg):
FILE: visualization/BetterMapVis_script_version_FLOW.py
function make_all_coords_arrays (line 17) | def make_all_coords_arrays(filtered_dfs):
function get_sprite_by_coords (line 20) | def get_sprite_by_coords(img, x, y):
function game_coord_to_global_coord (line 27) | def game_coord_to_global_coord(
function add_sprite (line 75) | def add_sprite(overlay_map, sprite, coord):
function blend_overlay (line 89) | def blend_overlay(background, over):
function split (line 95) | def split(img):
function compute_flow (line 98) | def compute_flow(all_coords, inter_steps=1, add_start=True):
function render_arrows (line 171) | def render_arrows(fname, all_flows, arrow_sprite):
function compute_flow_wrap (line 228) | def compute_flow_wrap(dat):
FILE: visualization/BetterMapVis_script_version_FLOW_edge.py
function make_all_coords_arrays (line 17) | def make_all_coords_arrays(filtered_dfs):
function get_sprite_by_coords (line 20) | def get_sprite_by_coords(img, x, y):
function game_coord_to_global_coord (line 27) | def game_coord_to_global_coord(
function add_sprite (line 75) | def add_sprite(overlay_map, sprite, coord):
function blend_overlay (line 89) | def blend_overlay(background, over):
function split (line 95) | def split(img):
function compute_flow (line 98) | def compute_flow(all_coords, inter_steps=1, add_start=True):
function render_arrows (line 171) | def render_arrows(fname, all_flows, arrow_sprite):
function compute_flow_wrap (line 258) | def compute_flow_wrap(dat):
FILE: visualization/BetterMapVis_script_version_PROG_COLOR.py
function make_all_coords_arrays (line 19) | def make_all_coords_arrays(filtered_dfs):
function load_tex (line 22) | def load_tex(name):
function get_sprite_by_coords (line 26) | def get_sprite_by_coords(img, x, y):
function game_coord_to_pixel_coord (line 33) | def game_coord_to_pixel_coord(
function add_sprite (line 81) | def add_sprite(overlay_map, sprite, coord):
function blend_overlay (line 95) | def blend_overlay(background, over):
function split (line 101) | def split(img):
function render_video (line 104) | def render_video(fname, all_coords, walks, bg, inter_steps=4, add_start=...
function test_render (line 177) | def test_render(name, dat, walks, bg):
Condensed preview — 59 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (8,221K chars).
[
{
"path": ".gitignore",
"chars": 156,
"preview": "__pycache__\n.DS_Store\nrollouts\n*.gb\n*.ram\n.ipynb_checkpoints\nraw_documentation\n*.pem\n_autoplay.mp4\nsession_*\n.env\nPokemo"
},
{
"path": "LICENSE",
"chars": 1070,
"preview": "MIT License\n\nCopyright (c) 2023 Peter Whidden\n\nPermission is hereby granted, free of charge, to any person obtaining a c"
},
{
"path": "README.md",
"chars": 5011,
"preview": "# Train RL agents to play Pokemon Red\n\n### New 10-19-24! Updated & Simplified V2 Training Script - See V2 below\n### New "
},
{
"path": "VisualizeProgress.ipynb",
"chars": 2498939,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 218,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": ["
},
{
"path": "baselines/Visualizations_over_time.ipynb",
"chars": 816257,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n "
},
{
"path": "baselines/agent_enabled.txt",
"chars": 4,
"preview": "yes\n"
},
{
"path": "baselines/baseline_fast_minimal.py",
"chars": 3290,
"preview": "from os.path import exists\nfrom pathlib import Path\nimport uuid\n\nfrom stable_baselines3 import PPO\nfrom stable_baselines"
},
{
"path": "baselines/delete_empty_imgs.txt",
"chars": 53,
"preview": "find . -name \"*.jpeg\" -type 'f' -size -1000c -delete\n"
},
{
"path": "baselines/events.json",
"chars": 63749,
"preview": "{\n \"0xD747-0\": \"Followed Oak Into Lab\",\n \"0xD747-1\": \"Event 001\",\n \"0xD747-2\": \"002\",\n \"0xD747-3\": \"Hall Of Fame Dex"
},
{
"path": "baselines/global_map.py",
"chars": 1078,
"preview": "# adapted from https://github.com/thatguy11325/pokemonred_puffer/blob/main/pokemonred_puffer/global_map.py\n\nimport os\nim"
},
{
"path": "baselines/grid_renders/parallel_scripts/parallel_grid_convert.sh",
"chars": 52,
"preview": "parallel --jobs 16 --progress bash ::: session_*.sh\n"
},
{
"path": "baselines/map_data.json",
"chars": 55844,
"preview": "{\n \"regions\": [\n {\n \"id\": \"-1\",\n \"name\": \"Kanto\",\n \"coordinates\": [\n "
},
{
"path": "baselines/memory_addresses.py",
"chars": 845,
"preview": "# addresses from https://datacrystal.romhacking.net/wiki/Pok%C3%A9mon_Red/Blue:RAM_map\n# https://github.com/pret/pokered"
},
{
"path": "baselines/ray_exp/red_gym_env_ray.py",
"chars": 20297,
"preview": "\nimport sys\nimport uuid \nimport os\nfrom math import floor, sqrt\nimport json\nfrom pathlib import Path\n\nimport logging\nlog"
},
{
"path": "baselines/ray_exp/requirements.txt",
"chars": 235,
"preview": "ray[default, rllib, tune, air] @ https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-3.0.0.dev0-cp310-cp310-manylin"
},
{
"path": "baselines/ray_exp/train_ray.py",
"chars": 1689,
"preview": "import uuid\nfrom pathlib import Path\nimport ray\nfrom ray.rllib.algorithms import ppo\nfrom red_gym_env_ray import RedGymE"
},
{
"path": "baselines/red_gym_env.py",
"chars": 24928,
"preview": "\nimport sys\nimport uuid \nimport os\nfrom math import floor, sqrt\nimport json\nfrom pathlib import Path\n\nimport numpy as np"
},
{
"path": "baselines/red_gym_env_minimal.py",
"chars": 16238,
"preview": "import uuid\nimport json\nfrom pathlib import Path\n\nimport numpy as np\nfrom skimage.transform import downscale_local_mean\n"
},
{
"path": "baselines/render_all_needed_grids.py",
"chars": 2869,
"preview": "from os.path import exists\nfrom pathlib import Path\nimport sys\nimport uuid\nfrom red_gym_env import RedGymEnv\nfrom stable"
},
{
"path": "baselines/render_all_needed_grids.sh",
"chars": 83,
"preview": "cat saves_to_record.txt | xargs -I {} sh -c \"python render_all_needed_grids.py {}\"\n"
},
{
"path": "baselines/requirements-unfrozen.txt",
"chars": 221,
"preview": "stable_baselines3\ngymnasium\ngym\nscikit-image\nscikit-learn\npandas\ntqdm\ntorch\neinops\nhnswlib\nnumpy\nopencv-python\nseaborn\np"
},
{
"path": "baselines/requirements.txt",
"chars": 1745,
"preview": "appnope==0.1.3\nasttokens==2.2.1\nattrs==23.1.0\nbackcall==0.2.0\nbeautifulsoup4==4.12.2\nbleach==6.0.0\ncloudpickle==2.2.1\nco"
},
{
"path": "baselines/run_baseline_parallel.py",
"chars": 2743,
"preview": "from os.path import exists\nfrom pathlib import Path\nimport uuid\nfrom red_gym_env import RedGymEnv\nfrom stable_baselines3"
},
{
"path": "baselines/run_baseline_parallel_fast.py",
"chars": 3193,
"preview": "from os.path import exists\nfrom pathlib import Path\nimport uuid\nfrom red_gym_env import RedGymEnv\nfrom stable_baselines3"
},
{
"path": "baselines/run_pretrained_interactive.py",
"chars": 2387,
"preview": "from os.path import exists\nfrom pathlib import Path\nimport uuid\nfrom red_gym_env import RedGymEnv\nfrom stable_baselines3"
},
{
"path": "baselines/run_recorded_actions.py",
"chars": 1233,
"preview": "from pathlib import Path\nimport pandas as pd\nimport numpy as np\nfrom red_gym_env import RedGymEnv\n\ndef run_recorded_acti"
},
{
"path": "baselines/saves_to_record.txt",
"chars": 2580,
"preview": "session_4da05e87/init\nsession_4da05e87/poke_720896_steps.zip\nsession_4da05e87/poke_1441792_steps.zip\nsession_4da05e87/po"
},
{
"path": "baselines/stream_agent_wrapper.py",
"chars": 2336,
"preview": "import asyncio\nimport websockets\nimport json\n\nimport gymnasium as gym\n\nX_POS_ADDRESS, Y_POS_ADDRESS = 0xD362, 0xD361\nMAP"
},
{
"path": "baselines/tensorboard_callback.py",
"chars": 2934,
"preview": "import os\nimport json\n\nfrom stable_baselines3.common.callbacks import BaseCallback\nfrom stable_baselines3.common.logger "
},
{
"path": "baselines/tile_vids_to_grid.py",
"chars": 3073,
"preview": "import sys\nimport stat\nimport subprocess\nfrom pathlib import Path\n\n# inspired by https://www.jimby.name/techbits/recent/"
},
{
"path": "clip_experiment/Interacting_with_CLIP_Pokemon.ipynb",
"chars": 1060775,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"YPHN7PJgKOzb\"\n },\n \"source\": [\n \"# Inte"
},
{
"path": "v2/agent_enabled.txt",
"chars": 4,
"preview": "yes\n"
},
{
"path": "v2/baseline_fast_v2.py",
"chars": 3730,
"preview": "import sys\nfrom os.path import exists\nfrom pathlib import Path\nfrom red_gym_env_v2 import RedGymEnv\nfrom stream_agent_wr"
},
{
"path": "v2/events.json",
"chars": 63749,
"preview": "{\n \"0xD747-0\": \"Followed Oak Into Lab\",\n \"0xD747-1\": \"Event 001\",\n \"0xD747-2\": \"002\",\n \"0xD747-3\": \"Hall Of Fame Dex"
},
{
"path": "v2/global_map.py",
"chars": 1078,
"preview": "# adapted from https://github.com/thatguy11325/pokemonred_puffer/blob/main/pokemonred_puffer/global_map.py\n\nimport os\nim"
},
{
"path": "v2/go_forever.sh",
"chars": 244,
"preview": "#!/bin/bash\n\nwhile true; do\n\t# latest file with .zip extension\n\tlatest_file=$(ls -lt runs/*.zip | awk 'NR==1 {print $9}'"
},
{
"path": "v2/macos_requirements.txt",
"chars": 1071,
"preview": "absl-py==2.1.0\nasttokens==2.4.1\ncloudpickle==3.1.0\ncontourpy==1.3.0\ncycler==0.12.1\ndecorator==5.1.1\neinops==0.8.0\nexecut"
},
{
"path": "v2/map_data.json",
"chars": 55844,
"preview": "{\n \"regions\": [\n {\n \"id\": \"-1\",\n \"name\": \"Kanto\",\n \"coordinates\": [\n "
},
{
"path": "v2/red_gym_env_v2.py",
"chars": 20941,
"preview": "import uuid\nimport json\nfrom pathlib import Path\n\nimport numpy as np\nfrom skimage.transform import downscale_local_mean\n"
},
{
"path": "v2/requirements.txt",
"chars": 1450,
"preview": "absl-py==2.1.0\nasttokens==2.4.1\ncloudpickle==3.1.0\ncontourpy==1.3.0\ncycler==0.12.1\ndecorator==5.1.1\neinops==0.8.0\nexecut"
},
{
"path": "v2/run_pretrained_interactive.py",
"chars": 3439,
"preview": "import os\nfrom os.path import exists\nfrom pathlib import Path\nimport uuid\nimport time\nimport glob\nfrom red_gym_env_v2 im"
},
{
"path": "v2/runs/.gitignore",
"chars": 0,
"preview": ""
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// ... and 5 more files (download for full content)
About this extraction
This page contains the full source code of the PWhiddy/PokemonRedExperiments GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 59 files (27.8 MB), approximately 2.0M tokens, and a symbol index with 218 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.