Chen-Yu Lu

Distributionally Robust Average-Reward Reinforcement Learning: Finite-Sample Guarantees under Weak Communication

This work establishes explicit radius conditions under which the robust average-reward Bellman equation admits a constant-gain solution, while for total variation and Wasserstein balls, any positive radius suffices without requiring the nominal MDP to be weakly communicating.