Reinforcement Learning
stable-baselines3
Acrobot-v1
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use JOSEFSUSON/ppo-Acrobot-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use JOSEFSUSON/ppo-Acrobot-v1 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="JOSEFSUSON/ppo-Acrobot-v1", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Pushing Simulation 9 Acrobot-v1 PPO model with high score for Excellence Certificate
6a8d5c9 verified |
Download README.md from JOSEFSUSON/ppo-Acrobot-v1: direct link, hf CLI and curl.
- Browser
- Download file 764 Bytes
-
https://huggingface.co/JOSEFSUSON/ppo-Acrobot-v1/resolve/main/README.md
- Command line
-
hf download hf://JOSEFSUSON/ppo-Acrobot-v1/README.md
-
curl -L -o README.md https://huggingface.co/JOSEFSUSON/ppo-Acrobot-v1/resolve/main/README.md
764 Bytes
| library_name: stable-baselines3 | |
| tags: | |
| - Acrobot-v1 | |
| - deep-reinforcement-learning | |
| - reinforcement-learning | |
| - stable-baselines3 | |
| model-index: | |
| - name: PPO | |
| results: | |
| - task: | |
| type: reinforcement-learning | |
| name: reinforcement-learning | |
| dataset: | |
| name: Acrobot-v1 | |
| type: Acrobot-v1 | |
| metrics: | |
| - type: mean_reward | |
| value: -86.50 +/- 18.00 | |
| name: mean_reward | |
| verified: false | |
| # **PPO** Agent playing **Acrobot-v1** | |
| This is a trained model of a **PPO** agent playing **Acrobot-v1** | |
| using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). | |
| ## Usage (with Stable-baselines3) | |
| TODO: Add your code | |
| ```python | |
| from stable_baselines3 import ... | |
| from huggingface_sb3 import load_from_hub | |
| ... | |
| ``` | |