• Corpus ID: 252596278

Human-AI Shared Control via Policy Dissection

@inproceedings{Li2022HumanAISC,
  title={Human-AI Shared Control via Policy Dissection},
  author={Quanyi Li and Zhenghao Peng and Haibin Wu and Lan Feng and Bolei Zhou},
  year={2022}
}
Human-AI shared control allows human to interact and collaborate with autonomous agents to accomplish control tasks in complex environments. Previous Reinforcement Learning (RL) methods attempted goal-conditioned designs to achieve human-controllable policies at the cost of redesigning the reward function and training paradigm. Inspired by the neuroscience approach to investigate the motor cortex in primates, we develop a simple yet effective frequency-based approach called Policy Dissection to… 

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