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You are here: Home Papers Approximate Linear Programming for Constrained Partially Observable Markov Decision Processes

Pascal Poupart, Aarti Malhotra, Pei Pei, Kee-Eung Kim, Bongseok Goh, and Michael Bowling (2015)

Approximate Linear Programming for Constrained Partially Observable Markov Decision Processes

In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).

In many situations, it is desirable to optimize a primary objective while respecting some constraints with respect to secondary objectives. In this work, we describe a technique based on approximate linear programming to optimize policies in constrained partially observable Markov decision processes. The optimization is performed offline and produces a finite state controller with desirable performance guarantees. The approach performs favorably in comparison to a constrained version of point-based value iteration on a suite of benchmark problems.