Mingyi Li

Policy Optimization Achieves Data-Dependent Regret Bounds in MDPs with Unknown Transitions

Mingyi Li, Taira Tsuchiya, Kenji Yamanishi

We study policy optimization for online episodic tabular Markov decision processes with unknown transition kernels, aiming for best-of-both-worlds guarantees together with data-dependent regret bounds. Recent work (Dann et al., 2023; Li et al., 2026) has shown that policy optimiz…Read more