Reinforcement Learning
stable-baselines3
Pendulum-v1
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use sb3/ddpg-Pendulum-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use sb3/ddpg-Pendulum-v1 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="sb3/ddpg-Pendulum-v1", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
| !!python/object/apply:collections.OrderedDict | |
| - - - buffer_size | |
| - 200000 | |
| - - gamma | |
| - 0.98 | |
| - - gradient_steps | |
| - -1 | |
| - - learning_rate | |
| - 0.001 | |
| - - learning_starts | |
| - 10000 | |
| - - n_timesteps | |
| - 20000 | |
| - - noise_std | |
| - 0.1 | |
| - - noise_type | |
| - normal | |
| - - policy | |
| - MlpPolicy | |
| - - policy_kwargs | |
| - dict(net_arch=[400, 300]) | |
| - - train_freq | |
| - - 1 | |
| - episode | |