Pessimistic Iterative Planning with RNNs for Robust POMDPs

Maris F. L. Galesloot, Marnix Suilen, Thiago D. Simão, Steven Carr, Matthijs T. J. Spaan, Ufuk Topcu, Nils Jansen

Abstract: Robust POMDPs extend classical POMDPs to incorporate model uncertainty using so-called uncertainty sets on the transition and observation functions, effectively defining ranges of probabilities. Policies for robust POMDPs must be (1) memory-based to account for partial observability and (2) robust against model uncertainty to account for the worst-case probability instances from the uncertainty sets. To compute such robust memory-based policies, we propose the pessimistic iterative planning (PIP) framework, which alternates between (1) selecting pessimistic POMDPs via worst-case probability instances from the uncertainty sets, and (2) computing finite-state controllers (FSCs) for these pessimistic POMDPs. Within PIP, we propose the RFSCNET algorithm, which optimizes a recurrent neural network to compute the FSCs. The empirical evaluation shows that RFSCNET can compute better-performing robust policies than several baselines and a state-of-the-art robust POMDP solver.

External IDs:dblp:conf/ecai/GaleslootSS0ST025