Robust Reinforcement Learning via Leveraging Historically Optimal Policy With Regulation of Performance

Summary

This paper proposes HORP, a robust reinforcement learning method that uses the historically optimal policy to guide policy optimization and generates diverse adversarial perturbations. It builds a guidance value function from value gaps and policy distribution divergence, and adds performance-aware correction and controlled uncertainty injection. Experiments report superior natural performance and robustness against various state attacks in most cases.

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