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|a Savla, Ketan
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|a Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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|a Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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|a Frazzoli, Emilio
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|a Frazzoli, Emilio
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|a Savla, Ketan
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|a Frazzoli, Emilio
|e author
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|a Game-theoretic learning algorithm for a spatial coverage problem
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|b Institute of Electrical and Electronics Engineers,
|c 2010-12-17T16:38:31Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/60305
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|a In this paper we consider a class of dynamic vehicle routing problems, in which a number of mobile agents in the plane must visit target points generated over time by a stochastic process. It is desired to design motion coordination strategies in order to minimize the expected time between the appearance of a target point and the time it is visited by one of the agents. We cast the problem as a spatial game in which each agent's objective is to maximize the expected value of the à ¿time spent aloneà ¿ at the next target location and show that the Nash equilibria of the game correspond to the desired agent configurations. We propose learning-based control strategies that, while making minimal or no assumptions on communications between agents as well as the underlying distribution, provide the same level of steady-state performance achieved by the best known decentralized strategies.
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|a en_US
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|a Article
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|t 47th Annual Allerton Conference on Communication, Control, and Computing, 2009. Allerton 2009
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