Optimal Fixed-Size Controllers for Decentralized POMDPs
Christopher Amato
Daniel S. Bernstein
Shlomo Zilberstein
Abstract
Solving decentralized partially observable Markov decision processes
(DEC-POMDPs)
is a difficult task. Exact solutions are intractable in all but the
smallest problems and
approximate solutions provide limited optimality guarantees. As a more
principled alter-
native, we present a novel formulation of an optimal fixed-size solution
of a DEC-POMDP
as a nonlinear program. We discuss the benefits of this representation
and evaluate sev-
eral optimization methods. While the methods used in this paper only
guarantee locally
optimal solutions, a wide range of powerful nonlinear optimization
techniques may now
be applied to this problem. We show that by using our formulation in
various domains,
solution quality is higher than a current state-of-the-art approach.
These results show
that optimization can be used to provide high quality solutions to
DEC-POMDPs while
maintaining moderate memory and time usage.
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