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README.md ADDED
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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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+
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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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+
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+ ## Usage (with ML-Agents)
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+ The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
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+
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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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+ - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
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+ browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
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+ - A *longer tutorial* to understand how works ML-Agents:
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+ https://huggingface.co/learn/deep-rl-course/unit5/introduction
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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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+
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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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+
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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: vpepe2003/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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+
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+ default_settings: null
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+ behaviors:
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+ SnowballTarget:
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+ trainer_type: ppo
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+ hyperparameters:
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+ batch_size: 128
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+ buffer_size: 2048
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+ learning_rate: 0.0003
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+ beta: 0.005
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+ epsilon: 0.2
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+ shared_critic: false
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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: 2
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+ vis_encode_type: simple
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+ memory: null
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+ goal_conditioning_type: hyper
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+ deterministic: false
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+ strength: 1.0
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+ network_settings:
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+ hidden_units: 128
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+ num_layers: 2
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+ memory: null
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+ goal_conditioning_type: hyper
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+ deterministic: false
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+ results_dir: results
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