Reinforcement Learning
stable-baselines3
seals/HalfCheetah-v0
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use HumanCompatibleAI/ppo-seals-HalfCheetah-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use HumanCompatibleAI/ppo-seals-HalfCheetah-v0 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="HumanCompatibleAI/ppo-seals-HalfCheetah-v0", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download config.yml from HumanCompatibleAI/ppo-seals-HalfCheetah-v0: direct link, hf CLI and curl.
- Browser
- Download file 805 Bytes
-
https://huggingface.co/HumanCompatibleAI/ppo-seals-HalfCheetah-v0/resolve/main/config.yml
- Command line
-
hf download hf://HumanCompatibleAI/ppo-seals-HalfCheetah-v0/config.yml
-
curl -L -o config.yml https://huggingface.co/HumanCompatibleAI/ppo-seals-HalfCheetah-v0/resolve/main/config.yml
805 Bytes
| !!python/object/apply:collections.OrderedDict | |
| - - - batch_size | |
| - 64 | |
| - - clip_range | |
| - 0.1 | |
| - - ent_coef | |
| - 3.794797423594763e-06 | |
| - - gae_lambda | |
| - 0.95 | |
| - - gamma | |
| - 0.95 | |
| - - learning_rate | |
| - 0.0003286871805949382 | |
| - - max_grad_norm | |
| - 0.8 | |
| - - n_envs | |
| - 1 | |
| - - n_epochs | |
| - 5 | |
| - - n_steps | |
| - 512 | |
| - - n_timesteps | |
| - 1000000.0 | |
| - - normalize | |
| - gamma: 0.95 | |
| norm_obs: false | |
| norm_reward: true | |
| - - policy | |
| - MlpPolicy | |
| - - policy_kwargs | |
| - activation_fn: !!python/name:torch.nn.modules.activation.Tanh '' | |
| features_extractor_class: !!python/name:imitation.policies.base.NormalizeFeaturesExtractor '' | |
| net_arch: | |
| - pi: | |
| - 64 | |
| - 64 | |
| vf: | |
| - 64 | |
| - 64 | |
| - - vf_coef | |
| - 0.11483689492120866 | |