Reinforcement Learning
stable-baselines3
Safetensors
halfcheetah
mujoco
sb3
sac
control
Eval Results (legacy)
Instructions to use lucasschott/HalfCheetah-v5-SAC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use lucasschott/HalfCheetah-v5-SAC with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="lucasschott/HalfCheetah-v5-SAC", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
SAC Agent for HalfCheetah-v5
This is a Soft Actor Critic (SAC) agent trained on the HalfCheetah-v5 environment using Stable Baselines 3.
Hyperparameters
See config.json for details.
Requirements
- Python: 3.10
Dependencies
gymnasium==1.0.0
gymnasium[mujoco]
torch==2.4.0
stable_baselines3==2.4.1
How to Load
from huggingface_hub import hf_hub_download
from stable_baselines3 import SAC
model_path = hf_hub_download(repo_id="lucasschott/HalfCheetah-v5-SAC", filename="model.zip")
agent = SAC.load(model_path)
- Downloads last month
- 4
Evaluation results
- mean_reward on HalfCheetah-v5self-reported14170.55 +/- 137.28