Instructions to use Subhash3008/m-agents-Pyramids with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ml-agents
How to use Subhash3008/m-agents-Pyramids with ml-agents:
mlagents-load-from-hf --repo-id="Subhash3008/m-agents-Pyramids" --local-dir="./downloads"
- Notebooks
- Google Colab
- Kaggle
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Download README.md from Subhash3008/m-agents-Pyramids: direct link, hf CLI and curl.
- Browser
- Download file 667 Bytes
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https://huggingface.co/Subhash3008/m-agents-Pyramids/resolve/main/README.md
- Command line
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hf download hf://Subhash3008/m-agents-Pyramids/README.md
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curl -L -o README.md https://huggingface.co/Subhash3008/m-agents-Pyramids/resolve/main/README.md
667 Bytes
| tags: | |
| - reinforcement-learning | |
| - ML-Agents-Pyramids | |
| - ml-agents | |
| - deep-rl-course | |
| model-index: | |
| - name: m-agents-Pyramids | |
| results: | |
| - task: | |
| type: reinforcement-learning | |
| name: reinforcement-learning | |
| dataset: | |
| name: ML-Agents-Pyramids | |
| type: ML-Agents-Pyramids | |
| metrics: | |
| - type: mean_reward | |
| value: 2.0 +/- 0.1 | |
| name: mean_reward | |
| # ML-Agents Pyramids | |
| This is a trained model for the **Hugging Face Deep Reinforcement Learning Course**. | |
| - **Environment**: `ML-Agents-Pyramids` | |
| - **Library**: `ml-agents` | |
| - **Evaluation Score**: `2.0 +/- 0.1` | |
| Trained and submitted by **Subhash3008**. | |