| | ---
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| | library_name: ml-agents
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| | tags:
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| | - Pyramids
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| | - deep-reinforcement-learning
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| | - reinforcement-learning
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| | - ML-Agents-Pyramids
|
| | ---
|
| |
|
| | # **ppo** Agent playing **Pyramids**
|
| | This is a trained model of a **ppo** agent playing **Pyramids**
|
| | using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
|
| |
|
| | ## Usage (with ML-Agents)
|
| | The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
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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:
|
| | - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
|
| | browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
|
| | - A *longer tutorial* to understand how works ML-Agents:
|
| | https://huggingface.co/learn/deep-rl-course/unit5/introduction
|
| |
|
| | ### 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: koushik2k3/Pyramids
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| | 3. Step 2: Select your *.nn /*.onnx file
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| | 4. Click on Watch the agent play 👀
|
| | |
