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
Download config.json from lucasschott/HalfCheetah-v5-SAC: direct link, hf CLI and curl.
- Browser
- Download file 447 Bytes
-
https://huggingface.co/lucasschott/HalfCheetah-v5-SAC/resolve/main/config.json
- Command line
-
hf download hf://lucasschott/HalfCheetah-v5-SAC/config.json
-
curl -L -o config.json https://huggingface.co/lucasschott/HalfCheetah-v5-SAC/resolve/main/config.json
447 Bytes
| { | |
| "total_timesteps": 20000000, | |
| "policy": "MlpPolicy", | |
| "policy_kwargs": { | |
| "log_std_init": -3, | |
| "activation_fn": "nn.ReLU", | |
| "net_arch": [256, 256] | |
| }, | |
| "learning_rate": "linear_schedule(1e-3,5e-4)", | |
| "batch_size": 256, | |
| "gamma": 0.99, | |
| "learning_starts": 10000, | |
| "buffer_size": 1000000, | |
| "tau": 0.005, | |
| "ent_coef": "auto", | |
| "train_freq": 1, | |
| "gradient_steps": 1, | |
| "use_sde": true | |
| } |