| ---
|
| library_name: ml-agents
|
| tags:
|
| - Pyramids
|
| - deep-reinforcement-learning
|
| - reinforcement-learning
|
| - ML-Agents-Pyramids
|
| - curiosity
|
| - RND
|
| ---
|
|
|
| # **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).
|
|
|
| This agent uses **Random Network Distillation (RND)** for curiosity-driven exploration to solve the Pyramids environment, where it must press a button to spawn a pyramid, navigate to it, knock it over, and reach the gold brick at the top.
|
|
|
| ## Usage (with ML-Agents)
|
| The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
|
|
|
| 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
|
| ```bash
|
| mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume |
