Reinforcement Learning
ml-agents
ONNX
ML-Agents-SoccerTwos
SoccerTwos
deep-rl-course
ppo
multi-agent
Eval Results (legacy)
Instructions to use Learnix-AI-Lab/ppo-SoccerTwos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ml-agents
How to use Learnix-AI-Lab/ppo-SoccerTwos with ml-agents:
mlagents-load-from-hf --repo-id="Learnix-AI-Lab/ppo-SoccerTwos" --local-dir="./downloads"
- Notebooks
- Google Colab
- Kaggle
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Download README.md from Learnix-AI-Lab/ppo-SoccerTwos: direct link, hf CLI and curl.
- Browser
- Download file 676 Bytes
-
https://huggingface.co/Learnix-AI-Lab/ppo-SoccerTwos/resolve/main/README.md
- Command line
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hf download hf://Learnix-AI-Lab/ppo-SoccerTwos/README.md
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curl -L -o README.md https://huggingface.co/Learnix-AI-Lab/ppo-SoccerTwos/resolve/main/README.md
676 Bytes
| tags: | |
| - ML-Agents-SoccerTwos | |
| - SoccerTwos | |
| - ml-agents | |
| - deep-rl-course | |
| - ppo | |
| - reinforcement-learning | |
| - multi-agent | |
| library_name: ml-agents | |
| pipeline_tag: reinforcement-learning | |
| model-index: | |
| - name: ppo-SoccerTwos | |
| results: | |
| - task: | |
| name: reinforcement-learning | |
| type: reinforcement-learning | |
| dataset: | |
| name: ML-Agents-SoccerTwos | |
| type: ML-Agents-SoccerTwos | |
| metrics: | |
| - name: mean_reward | |
| type: mean_reward | |
| value: 0.5 | |
| # PPO Agent playing SoccerTwos in Unity ML-Agents | |
| This is a trained multi-agent model playing SoccerTwos using Unity ML-Agents and Self-Play. | |
| - **Mean Reward**: 0.5 +/- 0.1 | |
| - **Result (mean - std)**: 0.40 | |