diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..a5a8dfab0838dfe48d177810e1471ca8ee2b62e6
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,134 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+runs
+wandb
+models
+
+
+# fichier mac
+.DS_Store
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
diff --git a/CartPole-v1.zip b/CartPole-v1.zip
new file mode 100644
index 0000000000000000000000000000000000000000..1355eae9b2f1eaca9ad3792c740ff7331587c03d
Binary files /dev/null and b/CartPole-v1.zip differ
diff --git a/README.md b/README.md
index 29da3e74e8aed76ea80f93dc1225dfeb83e45bea..1d483f66cd54c1d586b0147a4168c7ea459c3d2b 100644
--- a/README.md
+++ b/README.md
@@ -6,3 +6,8 @@ Link to the model on the hub :
# REINFORCE
Plot showing the total reward accross episodes: 
+
+# A2C trained model
+Link to the trained model (available on huggingFace): https://huggingface.co/oscarchaufour/a2c-CartPole-v1
+
+
diff --git a/a2c_sb3_cartpole.py b/a2c_sb3_cartpole.py
index d5090b4b11bf11d82f20f0e94b7a2a883e29e44c..9a1a519360c47cdc8bc7d4591c6f38649cad2339 100644
--- a/a2c_sb3_cartpole.py
+++ b/a2c_sb3_cartpole.py
@@ -1,53 +1,69 @@
-import gymnasium as gym
+import gym
import cv2
from stable_baselines3 import A2C
from huggingface_sb3 import package_to_hub, push_to_hub
from gym import envs
from gymnasium.envs.registration import register
from tqdm import tqdm
+import matplotlib.pyplot as plt
+import wandb
+from wandb.integration.sb3 import WandbCallback
+from stable_baselines3.common.vec_env import VecVideoRecorder
+import dill
+import zipfile
+# Initialize Weights & Biases
+total_timesteps = 10000
+config = {
+ "policy_type": "MlpPolicy",
+ "total_timesteps": total_timesteps,
+ "env_name": "CartPole-v1",
+}
+wandb.login()
+run = wandb.init(
+ project="a2c-cartpole-v1",
+ config=config,
+ sync_tensorboard=True, # auto-upload sb3's tensorboard metrics
+ monitor_gym=True, # auto-upload the videos of agents playing the game
+ save_code=True, # optional
+)
env_id = "CartPole-v1"
# Register the environment
register(id=env_id, entry_point='gym.envs.classic_control:CartPoleEnv', max_episode_steps=500)
env = gym.make(env_id)
+# env = VecVideoRecorder(
+# env,
+# f"videos/{run.id}",
+# record_video_trigger=lambda x: x % 2000 == 0,
+# video_length=200,
+# )
-model = A2C("MlpPolicy", env, verbose=1)
-model.learn(total_timesteps=10_000)
+model = A2C("MlpPolicy", env, verbose=1, tensorboard_log=f"runs/{run.id}")
+model.learn(total_timesteps=total_timesteps, callback=WandbCallback(
+ gradient_save_freq=100,
+ model_save_path=f"models/{run.id}"))
+
+
+# Mark the run as public in W&B project settings
+run.finish()
vec_env = model.get_env()
obs = vec_env.reset()
+
for i in tqdm(range(1000)):
action, _state = model.predict(obs, deterministic=True)
obs, reward, done, info = vec_env.step(action)
- vec_env.render("human")
-
-
-####### TO BE DONE #######
-
-# # Serialize the model and save it to a .zip file
-# import pickle
-# import zipfile
-
-# # Step 1: Serialize the model
-# model_bytes = pickle.dumps(model)
-
-# # Step 2: Create a .zip file containing the serialized model
-# zip_filename = env_id + ".zip"
-# with zipfile.ZipFile(zip_filename, 'w') as zipf:
-# zipf.writestr("model.pkl", model_bytes)
-
-# # Package and push the model to the Hugging Face Hub
-# model_package_id = package_to_hub(model=model,
-# model_name="a2c-CartPole-v1",
-# model_architecture="a2c",
-# env_id=env_id,
-# eval_env=env,
-# repo_id="oscarchaufour/a2c-CartPole-v1",
-# commit_message="Initial commit of A2C CartPole model")
-
-# # Push the model package to the Hub
-# push_to_hub(repo_id="oscarchaufour/a2c-CartPole-v1",
-# filename=model_package_id + ".zip",
-# commit_message="Added A2C CartPole model")
\ No newline at end of file
+ vec_env.render()
+
+def save_model(model, env_id):
+ # Step 1: Serialize the model
+ model_bytes = dill.dumps(model)
+
+ # Step 2: Create a .zip file containing the serialized model
+ zip_filename = env_id + ".zip"
+ with zipfile.ZipFile(zip_filename, 'w') as zipf:
+ zipf.writestr("model.pkl", model_bytes)
+
+
diff --git a/images/reinforce_rewards.png b/images/reinforce_rewards.png
index e76d632e17aa3bc917cd5e2de89c865baaeb0bcf..ad6ae6f4dcc6382e3da610e49588c4b51e50ccbd 100644
Binary files a/images/reinforce_rewards.png and b/images/reinforce_rewards.png differ
diff --git a/neural_network_model.zip b/neural_network_model.zip
deleted file mode 100644
index ba6de9a0b83bb0d2cf55768c2497c4f1a0e9345c..0000000000000000000000000000000000000000
Binary files a/neural_network_model.zip and /dev/null differ
diff --git a/reinforce_cartpole.py b/reinforce_cartpole.py
index bf5d1bb07e35868c96494835dad522ff77b29a78..ec767d8c70601339546b0d17c433cce76900af34 100644
--- a/reinforce_cartpole.py
+++ b/reinforce_cartpole.py
@@ -95,7 +95,7 @@ def plot_rewards(episodes_rewards):
if __name__ == "__main__":
# Create the environment
- env = gym.make("CartPole-v1")
+ env = gym.make("CartPole-v1", render_mode="human")
# Set up the agent
policy = Policy(
diff --git a/test.ipynb b/test.ipynb
deleted file mode 100644
index 8a16e6581015df166f4ea76288d775d5fc00158f..0000000000000000000000000000000000000000
--- a/test.ipynb
+++ /dev/null
@@ -1,769 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# TD 1 : Hands-On Reinforcement Learning"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Packages download"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Collecting pytorch\n",
- " Downloading pytorch-1.0.2.tar.gz (689 bytes)\n",
- " Installing build dependencies: started\n",
- " Installing build dependencies: finished with status 'done'\n",
- " Getting requirements to build wheel: started\n",
- " Getting requirements to build wheel: finished with status 'done'\n",
- " Installing backend dependencies: started\n",
- " Installing backend dependencies: finished with status 'done'\n",
- " Preparing metadata (pyproject.toml): started\n",
- " Preparing metadata (pyproject.toml): finished with status 'done'\n",
- "Building wheels for collected packages: pytorch\n",
- " Building wheel for pytorch (pyproject.toml): started\n",
- " Building wheel for pytorch (pyproject.toml): finished with status 'error'\n",
- "Failed to build pytorch\n",
- "Note: you may need to restart the kernel to use updated packages.\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " error: subprocess-exited-with-error\n",
- " \n",
- " × Building wheel for pytorch (pyproject.toml) did not run successfully.\n",
- " │ exit code: 1\n",
- " ╰─> [20 lines of output]\n",
- " Traceback (most recent call last):\n",
- " File \"c:\\Users\\oscar\\Documents\\GitHub\\mso_3_4-td1\\.venv\\Lib\\site-packages\\pip\\_vendor\\pyproject_hooks\\_in_process\\_in_process.py\", line 353, in <module>\n",
- " main()\n",
- " File \"c:\\Users\\oscar\\Documents\\GitHub\\mso_3_4-td1\\.venv\\Lib\\site-packages\\pip\\_vendor\\pyproject_hooks\\_in_process\\_in_process.py\", line 335, in main\n",
- " json_out['return_val'] = hook(**hook_input['kwargs'])\n",
- " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
- " File \"c:\\Users\\oscar\\Documents\\GitHub\\mso_3_4-td1\\.venv\\Lib\\site-packages\\pip\\_vendor\\pyproject_hooks\\_in_process\\_in_process.py\", line 251, in build_wheel\n",
- " return _build_backend().build_wheel(wheel_directory, config_settings,\n",
- " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
- " File \"C:\\Users\\oscar\\AppData\\Local\\Temp\\pip-build-env-onf33rce\\overlay\\Lib\\site-packages\\setuptools\\build_meta.py\", line 404, in build_wheel\n",
- " return self._build_with_temp_dir(\n",
- " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
- " File \"C:\\Users\\oscar\\AppData\\Local\\Temp\\pip-build-env-onf33rce\\overlay\\Lib\\site-packages\\setuptools\\build_meta.py\", line 389, in _build_with_temp_dir\n",
- " self.run_setup()\n",
- " File \"C:\\Users\\oscar\\AppData\\Local\\Temp\\pip-build-env-onf33rce\\overlay\\Lib\\site-packages\\setuptools\\build_meta.py\", line 480, in run_setup\n",
- " super(_BuildMetaLegacyBackend, self).run_setup(setup_script=setup_script)\n",
- " File \"C:\\Users\\oscar\\AppData\\Local\\Temp\\pip-build-env-onf33rce\\overlay\\Lib\\site-packages\\setuptools\\build_meta.py\", line 311, in run_setup\n",
- " exec(code, locals())\n",
- " File \"<string>\", line 15, in <module>\n",
- " Exception: You tried to install \"pytorch\". The package named for PyTorch is \"torch\"\n",
- " [end of output]\n",
- " \n",
- " note: This error originates from a subprocess, and is likely not a problem with pip.\n",
- " ERROR: Failed building wheel for pytorch\n",
- "ERROR: Could not build wheels for pytorch, which is required to install pyproject.toml-based projects\n"
- ]
- }
- ],
- "source": [
- "pip install pytorch"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Collecting gym==0.26.2Note: you may need to restart the kernel to use updated packages.\n",
- "\n",
- " Downloading gym-0.26.2.tar.gz (721 kB)\n",
- " ---------------------------------------- 0.0/721.7 kB ? eta -:--:--\n",
- " ---- ---------------------------------- 92.2/721.7 kB 2.6 MB/s eta 0:00:01\n",
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- " -------------------------------------- 721.7/721.7 kB 2.7 MB/s eta 0:00:00\n",
- " Installing build dependencies: started\n",
- " Installing build dependencies: finished with status 'done'\n",
- " Getting requirements to build wheel: started\n",
- " Getting requirements to build wheel: finished with status 'done'\n",
- " Installing backend dependencies: started\n",
- " Installing backend dependencies: finished with status 'done'\n",
- " Preparing metadata (pyproject.toml): started\n",
- " Preparing metadata (pyproject.toml): finished with status 'done'\n",
- "Collecting numpy>=1.18.0 (from gym==0.26.2)\n",
- " Downloading numpy-1.26.4-cp311-cp311-win_amd64.whl.metadata (61 kB)\n",
- " ---------------------------------------- 0.0/61.0 kB ? eta -:--:--\n",
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- "Collecting cloudpickle>=1.2.0 (from gym==0.26.2)\n",
- " Downloading cloudpickle-3.0.0-py3-none-any.whl.metadata (7.0 kB)\n",
- "Collecting gym-notices>=0.0.4 (from gym==0.26.2)\n",
- " Downloading gym_notices-0.0.8-py3-none-any.whl (3.0 kB)\n",
- "Downloading cloudpickle-3.0.0-py3-none-any.whl (20 kB)\n",
- "Downloading numpy-1.26.4-cp311-cp311-win_amd64.whl (15.8 MB)\n",
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- "Building wheels for collected packages: gym\n",
- " Building wheel for gym (pyproject.toml): started\n",
- " Building wheel for gym (pyproject.toml): finished with status 'done'\n",
- " Created wheel for gym: filename=gym-0.26.2-py3-none-any.whl size=827629 sha256=73a686c8633b88e54b0477d643a4d44b9c28378813129811ee402183300c3525\n",
- " Stored in directory: c:\\users\\oscar\\appdata\\local\\pip\\cache\\wheels\\1c\\77\\9e\\9af5470201a0b0543937933ee99ba884cd237d2faefe8f4d37\n",
- "Successfully built gym\n",
- "Installing collected packages: gym-notices, numpy, cloudpickle, gym\n",
- "Successfully installed cloudpickle-3.0.0 gym-0.26.2 gym-notices-0.0.8 numpy-1.26.4\n"
- ]
- }
- ],
- "source": [
- "pip install gym==0.26.2"
- ]
- },
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- "metadata": {},
- "outputs": [
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- "Collecting pyglet==2.0.10\n",
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- "Installing collected packages: pyglet\n",
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- "Note: you may need to restart the kernel to use updated packages.\n"
- ]
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- "source": [
- "pip install pyglet==2.0.10"
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- },
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- "cell_type": "code",
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- "metadata": {},
- "outputs": [
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- "output_type": "stream",
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- "Note: you may need to restart the kernel to use updated packages.\n"
- ]
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- "source": [
- "pip install pygame==2.5.2"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
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- "text": [
- "Collecting PyQt5Note: you may need to restart the kernel to use updated packages.\n",
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- "Installing collected packages: PyQt5-Qt5, PyQt5-sip, PyQt5\n",
- "Successfully installed PyQt5-5.15.10 PyQt5-Qt5-5.15.2 PyQt5-sip-12.13.0\n"
- ]
- }
- ],
- "source": [
- "pip install PyQt5"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Usage"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [],
- "source": [
- "import gym\n",
- "import time\n",
- "\n",
- "# Create the environment\n",
- "env = gym.make(\"CartPole-v1\", render_mode=\"human\")\n",
- "\n",
- "# Reset the environment and get the initial observation\n",
- "observation = env.reset()\n",
- "\n",
- "for _ in range(1000):\n",
- " # Select a random action from the action space\n",
- " action = env.action_space.sample()\n",
- " # Apply the action to the environment\n",
- " # Returns next observation, reward, done signal (indicating\n",
- " # if the episode has ended), and an additional info dictionary\n",
- " observation, reward, terminated, truncated, info = env.step(action)\n",
- " # Render the environment to visualize the agent's behavior\n",
- " env.render()\n",
- " if terminated: \n",
- " # Terminated before max step\n",
- " break\n",
- " time.sleep(0.2) # Sleep added to slow down the visualization\n",
- "\n",
- "env.close()\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Upload model to Hugging Face hub"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "ename": "NameError",
- "evalue": "name 'model' is not defined",
- "output_type": "error",
- "traceback": [
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[1;32mIn[8], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mhuggingface_sb3\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m package_to_hub, push_to_hub\n\u001b[1;32m----> 3\u001b[0m package_to_hub(model\u001b[38;5;241m=\u001b[39m\u001b[43mmodel\u001b[49m, \n\u001b[0;32m 4\u001b[0m model_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma2c-CartPole-v1\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 5\u001b[0m model_architecture\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma2c\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 6\u001b[0m env_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCartPole-v1\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 7\u001b[0m eval_env\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m 8\u001b[0m repo_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moscarchaufour/a2c-CartPole-v1\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 9\u001b[0m commit_message\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTest commit\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 11\u001b[0m push_to_hub(repo_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moscarchaufour/a2c-CartPole-v1\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 12\u001b[0m filename\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma2c_sb3_cartpole.zip\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 13\u001b[0m commit_message\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAdded A2C CartPole model\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
- "\u001b[1;31mNameError\u001b[0m: name 'model' is not defined"
- ]
- }
- ],
- "source": [
- "from huggingface_sb3 import package_to_hub, push_to_hub\n",
- "\n",
- "package_to_hub(model=model, \n",
- " model_name=\"a2c-CartPole-v1\",\n",
- " model_architecture=\"a2c\",\n",
- " env_id=\"CartPole-v1\",\n",
- " eval_env=None,\n",
- " repo_id=\"oscarchaufour/a2c-CartPole-v1\",\n",
- " commit_message=\"Test commit\")\n",
- " \n",
- "push_to_hub(repo_id=\"oscarchaufour/a2c-CartPole-v1\",\n",
- " filename=\"a2c_sb3_cartpole.zip\",\n",
- " commit_message=\"Added A2C CartPole model\")\n"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": ".venv",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}