Reinforcement Learning
stable-baselines3
Hopper-v5
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use farama-minari/Hopper-v5-SAC-expert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use farama-minari/Hopper-v5-SAC-expert with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="farama-minari/Hopper-v5-SAC-expert", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
SAC Agent playing Hopper-v5
This is a trained model of a SAC agent playing Hopper-v5 using the stable-baselines3 library.
Usage (with Stable-baselines3)
TODO: Add your code
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
- Downloads last month
- 130
Evaluation results
- mean_reward on Hopper-v5self-reported4098.17 +/- 247.70