| license: apache-2.0 | |
| tags: | |
| - reinforcement-learning | |
| - ml-agents | |
| - ML-Agents-SnowballTarget | |
| - deep-rl-class | |
| model-index: | |
| - name: SnowballTarget | |
| results: | |
| - task: | |
| type: reinforcement-learning | |
| name: Reinforcement Learning | |
| dataset: | |
| name: ML-Agents-SnowballTarget | |
| type: ML-Agents-SnowballTarget | |
| metrics: | |
| - type: mean_reward | |
| name: mean_reward | |
| value: 6.6364 +/- 2.0790 | |
| # SnowballTarget | |
| Trained from random initialization for Vlad Kolomiiets (`Vladprime`) using the official Hugging Face Deep RL Course Unity environment and Unity ML-Agents 1.1.0. No pretrained weights were used. This is an initial training run; it is not claimed to have converged. Units 5 and 7 require publishing a trained model without a reward threshold. | |
| Training: ppo, 30000 configured environment steps, seed 20261101. Export: genuine ML-Agents ONNX plus PyTorch checkpoint and trainer configuration. Separate ONNX evaluation process: seed 1000000; 44 completed agent episodes, mean 6.6364, standard deviation 2.0790. Original environment, action and observation spaces were retained. See results.json for individual returns and evaluation limitations. | |
| The .onnx file is compatible with the course Unity browser viewer. Training/evaluation code is in train-unity.py; the exact run configuration is config.yaml. | |