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--- |
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tags: |
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- unity-ml-agents |
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- ml-agents |
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- deep-reinforcement-learning |
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- reinforcement-learning |
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- ML-Agents-SnowballTarget |
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library_name: ml-agents |
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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** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). |
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## Usage (with ML-Agents) |
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The Documentation: https://github.com/huggingface/ml-agents#get-started |
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We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: |
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### Resume the training |
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``` |
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mlagents-learn ./config/ppo/SnowballTarget.yaml --run-id="SnowballTarget-v1" --resume |
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``` |
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### Training hyperparameters |
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```python |
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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: 5 |
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checkpoint_interval: 50000 |
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max_steps: 900000 |
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time_horizon: 128 |
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threaded: true |
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hyperparameters: |
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learning_rate: 0.0001 |
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learning_rate_schedule: linear |
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batch_size: 128 |
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buffer_size: 4096 |
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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: 5 |
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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: simple |
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reward_signals: |
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extrinsic: |
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gamma: 0.99 |
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strength: 1.0 |
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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. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget |
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2. Step 1: Write your model_id: kinkpunk/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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