metadata

tags:
  - Walker2d-v4
  - deep-reinforcement-learning
  - reinforcement-learning
  - custom-implementation
library_name: cleanrl
model-index:
  - name: SAC
    results:
      - task:
          type: reinforcement-learning
          name: reinforcement-learning
        dataset:
          name: Walker2d-v4
          type: Walker2d-v4
        metrics:
          - type: mean_reward
            value: 4735.99 +/- 18.89
            name: mean_reward
            verified: false

(CleanRL) SAC Agent Playing Walker2d-v4

This is a trained model of a SAC agent playing Walker2d-v4. The model was trained by using CleanRL and the most up-to-date training code can be found here.

Get Started

To use this model, please install the cleanrl package with the following command:

pip install "cleanrl[sac_continuous_action]"
python -m cleanrl_utils.enjoy --exp-name sac_continuous_action --env-id Walker2d-v4

Please refer to the documentation for more detail.

Command to reproduce the training

curl -OL https://huggingface.co/sdpkjc/Walker2d-v4-sac_continuous_action-seed5/raw/main/sac_continuous_action.py
curl -OL https://huggingface.co/sdpkjc/Walker2d-v4-sac_continuous_action-seed5/raw/main/pyproject.toml
curl -OL https://huggingface.co/sdpkjc/Walker2d-v4-sac_continuous_action-seed5/raw/main/poetry.lock
poetry install --all-extras
python sac_continuous_action.py --save-model --upload-model --hf-entity sdpkjc --env-id Walker2d-v4 --seed 5 --track

Hyperparameters

{'alpha': 0.2,
 'autotune': True,
 'batch_size': 256,
 'buffer_size': 1000000,
 'capture_video': False,
 'cuda': True,
 'env_id': 'Walker2d-v4',
 'exp_name': 'sac_continuous_action',
 'gamma': 0.99,
 'hf_entity': 'sdpkjc',
 'learning_starts': 5000.0,
 'noise_clip': 0.5,
 'policy_frequency': 2,
 'policy_lr': 0.0003,
 'q_lr': 0.001,
 'save_model': True,
 'seed': 5,
 'target_network_frequency': 1,
 'tau': 0.005,
 'torch_deterministic': True,
 'total_timesteps': 1000000,
 'track': True,
 'upload_model': True,
 'wandb_entity': None,
 'wandb_project_name': 'cleanRL'}