Adapting to Stochastic and Adversarial Losses in Episodic MDPs with Aggregate Bandit Feedback
This addresses the challenge of adapting to uncertain environments in reinforcement learning, offering a novel solution for scenarios with limited feedback, though it builds incrementally on prior work in online learning.
The paper tackles online learning in episodic MDPs with aggregate bandit feedback by proposing best-of-both-worlds algorithms that achieve O(log T) regret in stochastic settings and O(sqrt(T)) in adversarial ones, with matching lower bounds proving optimality.
We study online learning in finite-horizon episodic Markov decision processes (MDPs) under the challenging aggregate bandit feedback model, where the learner observes only the cumulative loss incurred in each episode, rather than individual losses at each state-action pair. While prior work in this setting has focused exclusively on worst-case analysis, we initiate the study of best-of-both-worlds (BOBW) algorithms that achieve low regret in both stochastic and adversarial environments. We propose the first BOBW algorithms for episodic tabular MDPs with aggregate bandit feedback. In the case of known transitions, our algorithms achieve $O(\log T)$ regret in stochastic settings and ${O}(\sqrt{T})$ regret in adversarial ones. Importantly, we also establish matching lower bounds, showing the optimality of our algorithms in this setting. We further extend our approach to unknown-transition settings by incorporating confidence-based techniques. Our results rely on a combination of FTRL over occupancy measures, self-bounding techniques, and new loss estimators inspired by recent advances in online shortest path problems. Along the way, we also provide the first individual-gap-dependent lower bounds and demonstrate near-optimal BOBW algorithms for shortest path problems with bandit feedback.