Empirical Policy Evaluation with Supergraphs
This provides a theoretical improvement for policy evaluation in RL, though it appears incremental as it builds on existing backward exploration ideas.
The paper tackles the empirical policy evaluation problem in reinforcement learning by developing backward exploration algorithms that reduce average-case sample complexity from O(S log S) to O(log S).
We devise and analyze algorithms for the empirical policy evaluation problem in reinforcement learning. Our algorithms explore backward from high-cost states to find high-value ones, in contrast to forward approaches that work forward from all states. While several papers have demonstrated the utility of backward exploration empirically, we conduct rigorous analyses which show that our algorithms can reduce average-case sample complexity from $O(S \log S)$ to as low as $O(\log S)$.