(CleanRL) DQN Agent Playing CartPole-v1
This is a trained model of a DQN agent playing CartPole-v1. 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[dqn]"
python -m cleanrl_utils.enjoy --exp-name dqn --env-id CartPole-v1
Please refer to the documentation for more detail.
Command to reproduce the training
curl -OL https://huggingface.co/jacksonhack/CartPole-v1-dqn-seed1/raw/main/dqn.py
curl -OL https://huggingface.co/jacksonhack/CartPole-v1-dqn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/jacksonhack/CartPole-v1-dqn-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqn.py --save-model --upload-model --total_timesteps 1000
Hyperparameters
{'batch_size': 128,
'buffer_size': 10000,
'capture_video': False,
'cuda': True,
'end_e': 0.05,
'env_id': 'CartPole-v1',
'exp_name': 'dqn',
'exploration_fraction': 0.5,
'gamma': 0.99,
'hf_entity': 'jacksonhack',
'learning_rate': 0.00025,
'learning_starts': 10000,
'num_envs': 1,
'save_model': True,
'seed': 1,
'start_e': 1,
'target_network_frequency': 500,
'tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 1000,
'track': False,
'train_frequency': 10,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
Evaluation results
- mean_reward on CartPole-v1self-reported88.80 +/- 47.92