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--- |
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library_name: ml-agents |
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tags: |
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- SnowballTarget |
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- deep-reinforcement-learning |
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- reinforcement-learning |
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- ML-Agents-SnowballTarget |
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--- |
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# **ppo** Agent playing **SnowballTarget** |
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This is a trained model of a **ppo** agent playing **SnowballTarget** |
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using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). |
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## Results |
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-[INFO] SnowballTarget. |
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-Step: 400000. |
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-Time Elapsed: 903.639 s. |
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-Mean Reward: 25.591. |
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-Std of Reward: 1.992. |
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## Hyperparameters |
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%%file /content/ml-agents/config/ppo/SnowballTarget.yaml |
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```yaml |
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behaviors: |
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SnowballTarget: |
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trainer_type: ppo |
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summary_freq: 10000 |
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keep_checkpoints: 10 |
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checkpoint_interval: 50000 |
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max_steps: 400000 |
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time_horizon: 32 |
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threaded: true |
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hyperparameters: |
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learning_rate: 0.0003 |
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learning_rate_schedule: linear |
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batch_size: 128 |
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buffer_size: 2048 |
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beta: 0.005 |
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epsilon: 0.2 |
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lambd: 0.95 |
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num_epoch: 3 |
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network_settings: |
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normalize: false |
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hidden_units: 256 |
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num_layers: 3 |
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vis_encode_type: nature_cnn |
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reward_signals: |
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extrinsic: |
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gamma: 0.9 |
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strength: 1.0 |
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``` |
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### Resume the training |
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```bash |
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mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume |
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``` |
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### Watch your Agent play |
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You can watch your agent **playing directly in your browser** |
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1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity |
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2. Step 1: Find your model_id: enrique2701/ppo-SnowballTarget |
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3. Step 2: Select your *.nn /*.onnx file |
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4. Click on Watch the agent play 👀 |
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