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README.md

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+# Reinforcement Learning with Stable Baselines3
+
+This is a simple example of using Stable Baselines3, a library for reinforcement learning, to train an agent on the CartPole-v0 environment.
+
+![CartPole](cartpole.gif)
+
+## Dependencies
+Make sure you have the following dependencies installed:
+- stable-baselines3
+- gym
+- pyglet
+
+You can install them using pip:
+```
+pip install stable-baselines3[extra]
+pip install pyglet==1.5.27
+```
+
+## Load Environment
+First, we import the necessary dependencies and create an instance of the CartPole-v0 environment:
+```python
+import os
+import gym
+from stable_baselines3 import PPO
+from stable_baselines3.common.vec_env import DummyVecEnv
+from stable_baselines3.common.evaluation import evaluate_policy
+
+environment_name = 'CartPole-v0'
+env = gym.make(environment_name)
+```
+
+## Training
+To train the agent, we initialize the PPO algorithm and pass in the environment. We then call the `learn` method to start the training process:
+```python
+log_path = os.path.join('Training', 'Logs')
+env = DummyVecEnv([lambda: env])
+model = PPO('MlpPolicy', env, verbose=1, tensorboard_log=log_path)
+model.learn(total_timesteps=20000)
+```
+
+## Save and Load the Model
+You can save the trained model to a file and load it later for evaluation or further training:
+```python
+PPO_Path = os.path.join('Training', 'Saved Models', 'PPO_Model_Cartpole')
+model.save(PPO_Path)
+model = PPO.load(PPO_Path, env=env)
+```
+
+## Evaluation
+To evaluate the performance of the trained agent, you can use the `evaluate_policy` function:
+```python
+evaluate_policy(model, env, n_eval_episodes=10, render=True)
+```
+
+## Testing the Model
+You can test the trained model by running episodes and observing its behavior:
+```python
+episodes = 5
+for episode in range(1, episodes + 1):
+    obs = env.reset()
+    done = False
+    score = 0
+
+    while not done:
+        env.render()
+        action, _ = model.predict(obs)
+        obs, reward, done, info = env.step(action)
+        score += reward
+
+    print('Episode: {} Score: {}'.format(episode, score))
+
+env.close()
+```
+
+## Viewing Training Logs
+You can visualize the training progress using TensorBoard. First, specify the log directory and start TensorBoard:
+```python
+training_log_path = os.path.join(log_path, 'PPO_1')
+!tensorboard --logdir={training_log_path}
+```
+Then, open `localhost:6006` in your browser to view the training logs.
+
+## Conclusion
+Reinforcement learning with Stable Baselines3 is a powerful tool for training agents in various environments. By following the steps in this example, you can train, save, and evaluate a reinforcement learning agent for the CartPole-v0 environment.