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poca-SoccerTwos
PhysHunter/poca-SoccerTwos
poca-SoccerTwos is a reinforcement learning model from PhysHunter. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. It is set up for ml-agents.
This is a trained model of a poca agent playing SoccerTwos using the Unity ML-Agents Library.
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Repo size
130 MB
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.onnx1.8 MB · 98%
At a glance
- Task
- Reinforcement Learning
- Library
- ml-agents
- Access
- Public
- Created
- Jun 9, 2023
- Updated
- Jun 10, 2023
- SHA
- 1a099f67
Model card
From the Hugging Face model README
poca Agent playing SoccerTwos
This is a trained model of a poca agent playing SoccerTwos using the Unity ML-Agents Library.
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
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
Watch your Agent play
You can watch your agent playing directly in your browser
- If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
- Step 1: Find your model_id: PhysHunter/poca-SoccerTwos
- Step 2: Select your .nn /.onnx file
- Click on Watch the agent play 👀
- Task
- Reinforcement Learning
- Library
- ml-agents
- Created
- Jun 9, 2023
- Updated
- Jun 10, 2023