Toward error-bounded algorithms for infinite-horizon DEC-POMDPs
Résumé
Over the past few years, attempts to scale up infinite-horizon DECPOMDPs are mainly due to approximate algorithms, but without the theoretical guarantees of their exact counterparts. In contrast, "-optimal methods have only theoretical significance but are not efficient in practice. In this paper, we introduce an algorithmic framework ( -PI) that exploits the scalability of the former while preserving the theoretical properties of the latter. We build upon -PI a family of approximate algorithms that can find (provably) errorbounded solutions in reasonable time. Among this family, H-PI uses a branch-and-bound search method that computes a near-optimal solution over distributions over histories experienced by the agents. These distributions often lie near structured, low-dimensional subspace embedded in the high-dimensional sufficient statistic. By planning only on this subspace, H-PI successfully solves all tested benchmarks, outperforming standard algorithms, both in solution time and policy quality.
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