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nsanghi/MountainCarContinuous-v0-ddpg_continuous_action-seed1
MountainCarContinuous-v0-ddpg_continuous_action-seed1 is a reinforcement learning model from nsanghi. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. It is set up for cleanrl.
This is a trained model of a DDPG agent playing MountainCarContinuous-v0. The model was trained by using CleanRL and the most up-to-date training code can be found here.
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Updated Jan 13, 2024
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From the Hugging Face model README
This is a trained model of a DDPG agent playing MountainCarContinuous-v0. The model was trained by using CleanRL and the most up-to-date training code can be found here.
To use this model, please install the cleanrl package with the following command:
pip install "cleanrl[ddpg_continuous_action]"
python -m cleanrl_utils.enjoy --exp-name ddpg_continuous_action --env-id MountainCarContinuous-v0
Please refer to the documentation for more detail.
curl -OL https://huggingface.co/nsanghi/MountainCarContinuous-v0-ddpg_continuous_action-seed1/raw/main/ddpg_continuous_action.py
curl -OL https://huggingface.co/nsanghi/MountainCarContinuous-v0-ddpg_continuous_action-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/nsanghi/MountainCarContinuous-v0-ddpg_continuous_action-seed1/raw/main/poetry.lock
poetry install --all-extras
python ddpg_continuous_action.py --no-cuda --total-timesteps 25000 --learning-starts 5000 --env-id MountainCarContinuous-v0 --track --hf-entity nsanghi --capture-video --save-model --upload-model
{'batch_size': 256,
'buffer_size': 1000000,
'capture_video': True,
'cuda': False,
'env_id': 'MountainCarContinuous-v0',
'exp_name': 'ddpg_continuous_action',
'exploration_noise': 0.1,
'gamma': 0.99,
'hf_entity': 'nsanghi',
'learning_rate': 0.0003,
'learning_starts': 5000,
'noise_clip': 0.5,
'policy_frequency': 2,
'save_model': True,
'seed': 1,
'tau': 0.005,
'torch_deterministic': True,
'total_timesteps': 25000,
'track': True,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}