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Corruption-Robust Offline Two-Player Zero-Sum Markov Games
We note that we are the first to provide such a characterization of the problem of learning approximate Nash Equilibrium policies in offline two-player zero-sum Markov games under data corruption.
Reward Model Learning vs. Direct Policy Optimization: A Comparative Analysis of Learning from Human Preferences
Moreover, we extend our analysis to the approximate optimization setting and derive exponentially decaying convergence rates for both RLHF and DPO.
Corruption Robust Offline Reinforcement Learning with Human Feedback
We aim to design algorithms that identify a near-optimal policy from the corrupted data, with provable guarantees.
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