Efficient Exploration in Resource-Restricted Reinforcement Learning

This is the code of paper Efficient Exploration in Resource-Restricted Reinforcement Learning . Zhihai Wang, Taoxing Pan, Qi Zhou, Jie Wang. AAAI 2023. [arXiv]

Requirements

• Python 3.6.9
• PyTorch 1.10
• tqdm
• gym 0.21
• mujoco 1.50

pip install -r requirements.txt

Reproduce the Results

1. For example, run experiments on Ant

python scripts/run.py configs/surprise_based/surprise_vision.json

Citation

If you find this code useful, please consider citing the following paper.

@misc{wang2022efficient,
      title={Efficient Exploration in Resource-Restricted Reinforcement Learning}, 
      author={Zhihai Wang and Taoxing Pan and Qi Zhou and Jie Wang},
      year={2022},
      eprint={2212.06988},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Other Repositories

If you are interested in our work, you may find the following papers useful.

Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization. Qi zhou, Houqiang Li, Jie Wang.* AAAI 2020. [paper] [code]

Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Critic. Zhihai Wang, Jie Wang, Qi Zhou, Bin Li, Houqiang Li.* AAAI 2022. [paper] [code]