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README.md

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+# PPO SnowballTarget Agent
+
+This model was trained using Proximal Policy Optimization (PPO) with Unity ML-Agents as part of the Hugging Face Deep Reinforcement Learning Course.
+
+## Environment
+- Unity ML-Agents
+- SnowballTarget environment
+
+## Training Details
+- Algorithm: PPO
+- Total training steps: 200,000
+- Final mean reward: ~23.2
+
+## Results
+The agent learned to consistently hit targets in the SnowballTarget environment and achieved stable rewards during training.
+
+Final training logs:
+- Step 160000 → Mean Reward: 22.84
+- Step 170000 → Mean Reward: 22.85
+- Step 180000 → Mean Reward: 23.00
+- Step 190000 → Mean Reward: 23.46
+- Step 200000 → Mean Reward: 23.21
+
+## Files
+- `SnowballTarget.onnx` — trained Unity ML-Agents policy network
+
+## Usage
+This model can be loaded into Unity ML-Agents for inference and evaluation.
+
+## Author
+Ryan Aparicio