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README.md

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+
+# PPO CartPole Agent
+
+This is a PPO agent trained on the CartPole-v1 environment using Stable Baselines3.
+
+## Performance
+
+The agent achieved a mean reward of 500.00 ± 0.00 over 10 evaluation episodes.
+
+## Training Details
+
+- Algorithm: PPO
+- Environment: CartPole-v1
+- Training Steps: 25,000
+- Framework: Stable Baselines3
+
+## Usage
+
+```python
+from stable_baselines3 import PPO
+import gymnasium as gym
+
+# Load the model
+model = PPO.load("drap/cartpole-ppo")
+
+# Create environment
+env = gym.make("CartPole-v1")
+
+# Test the model
+obs, _ = env.reset()
+while True:
+    action, _ = model.predict(obs, deterministic=True)
+    obs, reward, terminated, truncated, _ = env.step(action)
+    if terminated or truncated:
+        break
+```