Problem
Research questions and friction points this paper is trying to address.
RPOMDPs
omega-regular objectives
uncertainty sets
POSGs
computational complexity
Solving Robust POMDPs with Omega-regular Objectives via Partially Observable Stochastic Games
🤖 AI Summary
本文研究了通过部分可观测随机博弈解决具有Omega-正则目标的鲁棒POMDP问题，特别是在状态动作矩形和多面体不确定性集条件下。
📝 Abstract
Robust POMDPs (RPOMDPs) generalize classical POMDPs to the setting where exact transition probabilities are not known -- rather, they are only known to belong to some uncertainty set of values. In this work, we study the problem of solving RPOMDPs with general omega-regular objectives, which subsume a broad class of objectives such as reachability, safety, and linear temporal logic (LTL) objectives. We show that, for (s,a)-rectangular RPOMDPs with polytopic uncertainty sets, the problem of solving RPOMDPs under omega-regular objectives can be reduced to solving partially observable stochastic games (POSGs) under omega-regular objectives. Moreover, we show for the first time that reductions can be constructed in both directions, establishing the semantic equivalence between (s,a)-rectangular RPOMDPs with polytopic uncertainty sets and POSGs. This allows us to derive a range of new computational complexity results, including both upper and lower complexity bounds, on solving RPOMDPs with different omega-regular objectives. As a corollary, we also derive new computational complexity results for RMDPs.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Robust POMDPs
Omega-regular Objectives
Partially Observable Stochastic Games
Polytopic Uncertainty Sets
Computational Complexity
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