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Graphical Partially Observable Monte-Carlo Planning

30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain, 9 december
 
: Pfrommer, J.

:
Volltext urn:nbn:de:0011-n-4319393 (266 KByte PDF)
MD5 Fingerprint: fb00ccefdad402bce3836181039ddf5c
Erstellt am: 22.2.2017


2016, 8 S.
Annual Conference on Neural Information Processing Systems (NIPS) <30, 2016, Barcelona>
Englisch
Vortrag, Elektronische Publikation
Fraunhofer IOSB ()

Abstract
Monte-Carlo Tree Search (MCTS) techniques are state-of-the-art for online planning in Partially Observable Markov Decision Problems (POMDP). The recently proposed Factored-Value Multiagent POMCP (FV-MPOMCP) algorithm improves on the scalability of MCTS in Multiagent POMDP (MPOMDP) environments
by estimating several Q-values, each considering a subset of the actions and observations, and combining these Q-values via Variable Elimination. However, in MPOMDP, only the cumulated reward for each step is known, with no insight
on the reward structure. In this work, we additionally exploit the structure of reward that decomposes into local reward terms. The proposed Graphical Partially Observable Monte-Carlo Planning (GPOMCP) algorithm combines Monte-Carlo Tree Search with a variation of the message passing algorithm (Max-Sum) known from Graphical Probabilistic Models and Distributed Constraint Optimization.

: http://publica.fraunhofer.de/dokumente/N-431939.html