Optimal Non-Asymptotic Rates of Value Iteration for Average-Reward Markov Decision Processes

While there is an extensive body of research on the analysis of Value Iteration (VI) for discounted cumulative-reward MDPs, prior work on analyzing VI for (undiscounted) average-reward MDPs has been limited, and most prior results focus on asymptotic rates in terms of Bellman error. In this work, we conduct refined non-asymptotic analyses of average-reward MDPs, obtaining a collection of convergence results that advance our understanding of the setup. Among our new results, most notable are the O(1/k)-rates of Anchored Value Iteration on the Bellman error under the multichain setup and the span-based complexity lower bound that matches the O(1/k) upper bound up to a constant factor of 8 in the weakly communicating and unichain setups.