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FLAIRS
2009

Dynamic Programming Approximations for Partially Observable Stochastic Games

8 years 4 months ago
Dynamic Programming Approximations for Partially Observable Stochastic Games
Partially observable stochastic games (POSGs) provide a rich mathematical framework for planning under uncertainty by a group of agents. However, this modeling advantage comes with a price, namely a high computational cost. Solving POSGs optimally quickly becomes intractable after a few decision cycles. Our main contribution is to provide bounded approximation techniques, which enable us to scale POSG algorithms by several orders of magnitude. We study both the POSG model and its cooperative counterpart, DEC-POMDP. Experiments on a number of problems confirm the scalability of our approach while still providing useful policies.
Akshat Kumar, Shlomo Zilberstein
Added 17 Feb 2011
Updated 17 Feb 2011
Type Journal
Year 2009
Where FLAIRS
Authors Akshat Kumar, Shlomo Zilberstein
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