(CleanRL) TD3 Agent Playing Hopper-v5
This is a trained model of a TD3 agent playing Hopper-v5. 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[td3]"
python -m cleanrl_utils.enjoy --exp-name td3 --env-id Hopper-v5
Please refer to the documentation for more detail.
Command to reproduce the training
curl -OL https://huggingface.co/jacksonhack/Hopper-v5-td3-seed1/raw/main/td3.py
curl -OL https://huggingface.co/jacksonhack/Hopper-v5-td3-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/jacksonhack/Hopper-v5-td3-seed1/raw/main/poetry.lock
poetry install --all-extras
python td3.py --save_model --upload_model --track
Hyperparameters
{'batch_size': 256,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'env_id': 'Hopper-v5',
'exp_name': 'td3',
'exploration_noise': 0.1,
'gamma': 0.99,
'hf_entity': 'jacksonhack',
'learning_rate': 0.0003,
'learning_starts': 25000.0,
'noise_clip': 0.5,
'policy_frequency': 2,
'policy_noise': 0.2,
'save_model': True,
'seed': 1,
'tau': 0.005,
'torch_deterministic': True,
'total_timesteps': 1000000,
'track': True,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
Evaluation results
- mean_reward on Hopper-v5self-reported1329.44 +/- 156.77