Reinforcement Learning
stable-baselines3
PandaReachDense-v3
PandaReachDense
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use SimhaSimha/a2c-PandaReachDense-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use SimhaSimha/a2c-PandaReachDense-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="SimhaSimha/a2c-PandaReachDense-v3", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
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Download README.md from SimhaSimha/a2c-PandaReachDense-v3: direct link, hf CLI and curl.
- Browser
- Download file 742 Bytes
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https://huggingface.co/SimhaSimha/a2c-PandaReachDense-v3/resolve/main/README.md
- Command line
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hf download hf://SimhaSimha/a2c-PandaReachDense-v3/README.md
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curl -L -o README.md https://huggingface.co/SimhaSimha/a2c-PandaReachDense-v3/resolve/main/README.md
742 Bytes
| library_name: stable-baselines3 | |
| tags: | |
| - PandaReachDense-v3 | |
| - PandaReachDense | |
| - deep-reinforcement-learning | |
| - reinforcement-learning | |
| - stable-baselines3 | |
| model-index: | |
| - name: A2C | |
| results: | |
| - task: | |
| type: reinforcement-learning | |
| name: reinforcement-learning | |
| dataset: | |
| name: PandaReachDense-v3 | |
| type: PandaReachDense-v3 | |
| metrics: | |
| - type: mean_reward | |
| value: -0.32 +/- 0.13 | |
| name: mean_reward | |
| verified: false | |
| # **A2C** Agent playing **PandaReachDense-v3** | |
| This is a trained model of an **A2C** agent playing **PandaReachDense-v3** | |
| using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) for Unit 6 of the Hugging Face Deep RL Course. | |