Yuejie Chi

Robust Average-Reward Markov Decision Processes: Minimax-Optimal Learning via Plug-in Reductions

This work studies how many samples are necessary and sufficient to learn an $\varepsilon$-optimal robust policy under the average-reward criterion and achieves these rates using reduction-based plug-in procedures that select the reduction---nominal or robust---and its discount factor.