Point-based bounded policy iteration for decentralized POMDPs

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We present a memory-bounded approximate algorithm for solving infinite-horizon decentralized partially observable Markov decision processes (DEC-POMDPs). In particular, we improve upon the bounded policy iteration (BPI) approach, which searches for a locally optimal stochastic finite state controller, by accompanying reachability analysis on controller nodes. As a result, the algorithm has different optimization criteria for the reachable and the unreachable nodes, and it is more effective in the search for an optimal policy. Through experiments on benchmark problems, we show that our algorithm is competitive to the recent nonlinear optimization approach, both in the solution time and the policy quality.
Issue Date
2010-08-30
Language
ENG
Citation

11th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2010, pp.614 - 619

ISSN
0302-9743
URI
http://hdl.handle.net/10203/169081
Appears in Collection
CS-Conference Papers(학술회의논문)
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