Policy Optimization Achieves Data-Dependent Regret Bounds in MDPs with Unknown Transitions
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 optimization can adapt to both adversarial and stochastic losses with first-order, second-order, and path-length bounds, but only under known transitions, leaving open whether such data-dependent guarantees are achievable by policy optimization when the transition kernel is unknown. We resolve this by developing a new algorithm based on optimistic follow-the-regularized-leader that attains these guarantees under unknown ...
| Authors | Mingyi Li, Taira Tsuchiya, Kenji Yamanishi |
|---|---|
| Primary category | cs.LG |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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