Corruption-Robust Offline Reinforcement Learning with General Function Approximation

arXiv:2310.14550v3 Announce Type: replace
Abstract: We investigate the problem of corruption robustness in offline reinforcement learning (RL) with general function approximation, where an adversary can corrupt each sample in the offline dataset, and the corruption level $\zeta\geq0$ quantifies the cumulative corruption amount over $n$ episodes and $H$ steps. Our goal is to find a policy that is robust to such corruption and minimizes the suboptimality gap with respect to the optimal policy for the uncorrupted Markov decision processes (MDPs). Drawing …

abstract approximation arxiv corruption cs.lg dataset episodes function general offline reinforcement reinforcement learning robust robustness sample type