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2007
Springer

Solving Decentralized Continuous Markov Decision Problems with Structured Reward

8 years 7 months ago
Solving Decentralized Continuous Markov Decision Problems with Structured Reward
We present an approximation method that solves a class of Decentralized hybrid Markov Decision Processes (DEC-HMDPs). These DEC-HMDPs have both discrete and continuous state variables and represent individual agents with continuous measurable state-space, such as resources. Adding to the natural complexity of decentralized problems, continuous state variables lead to a blowup in potential decision points. Representing value functions as Rectangular Piecewise Constant (RPWC) functions, we formalize and detail an extension to the Coverage Set Algorithm (CSA) [1] that solves transition independent DECHMDPs with controlled error. We apply our algorithm to a range of multi-robot exploration problems with continuous resource constraints.
Emmanuel Benazera
Added 16 Dec 2010
Updated 16 Dec 2010
Type Journal
Year 2007
Where KI
Authors Emmanuel Benazera
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