Solving Concurrent Markov Decision Processes

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Solving Concurrent Markov Decision Processes
Typically, Markov decision problems (MDPs) assume a single action is executed per decision epoch, but in the real world one may frequently execute certain actions in parallel. This paper explores concurrent MDPs, MDPs which allow multiple non-conflicting actions to be executed simultaneously, and presents two new algorithms. Our first approach exploits two provably sound pruning rules, and thus guarantees solution optimality. Our second technique is a fast, samplingbased algorithm, which produces close-to-optimal solutions extremely quickly. Experiments show that our approaches outperform the existing algorithms producing up to two orders of magnitude speedup.
Mausam, Daniel S. Weld
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2004
Where AAAI
Authors Mausam, Daniel S. Weld
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