Reinforcement Learning
stable-baselines3
Pendulum-v1
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use sb3/ppo-Pendulum-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use sb3/ppo-Pendulum-v1 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="sb3/ppo-Pendulum-v1", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download config.yml from sb3/ppo-Pendulum-v1: direct link, hf CLI and curl.
- Browser
- Download file 367 Bytes
-
https://huggingface.co/sb3/ppo-Pendulum-v1/resolve/main/config.yml
- Command line
-
hf download hf://sb3/ppo-Pendulum-v1/config.yml
-
curl -L -o config.yml https://huggingface.co/sb3/ppo-Pendulum-v1/resolve/main/config.yml
367 Bytes
| !!python/object/apply:collections.OrderedDict | |
| - - - clip_range | |
| - 0.2 | |
| - - ent_coef | |
| - 0.0 | |
| - - gae_lambda | |
| - 0.95 | |
| - - gamma | |
| - 0.9 | |
| - - learning_rate | |
| - 0.001 | |
| - - n_envs | |
| - 4 | |
| - - n_epochs | |
| - 10 | |
| - - n_steps | |
| - 1024 | |
| - - n_timesteps | |
| - 100000.0 | |
| - - policy | |
| - MlpPolicy | |
| - - sde_sample_freq | |
| - 4 | |
| - - use_sde | |
| - true | |