| --- |
| library_name: ml-agents |
| tags: |
| - reinforcement-learning |
| - deep-reinforcement-learning |
| - SnowballTarget |
| - ML-Agents-SnowballTarget |
| model-index: |
| - name: ppo-SnowballTarget |
| results: |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: ML-Agents-SnowballTarget |
| type: ML-Agents-SnowballTarget |
| metrics: |
| - name: mean_reward |
| type: mean_reward |
| value: 24.75 +/- 2.384848003542364 |
| --- |
| # PPO agent playing SnowballTarget |
|
|
| Trained from scratch for 91,608 steps in Google Colab using Unity ML-Agents, with coding and execution assistance from Codex. This is an introductory course model, not a fully converged policy. |
|
|
| ## Evaluation |
| Evaluated using the exported ONNX policy with deterministic actions over 20 completed agent episodes, seed 12345. Mean reward: 24.75; standard deviation: 2.384848003542364. Soccer rewards include the team reward. Full episode returns are in evaluation.json. |
|
|
| ## Files |
| The ONNX file is the evaluated policy. configuration.yaml and config.json contain training settings. checkpoint.pt permits continued training; training.log records the actual run. |
|
|
| ## References |
| - https://huggingface.co/learn/deep-rl-course/en/unit5/hands-on |
| - https://huggingface.co/learn/deep-rl-course/en/unit7/hands-on |
| - https://github.com/Unity-Technologies/ml-agents |
|
|
