A Distributed Algorithm for Solving a Class of Multi-agent Markov Decision Problems
| dc.contributor.advisor | Fu, Michael C. | en_US |
| dc.contributor.author | Chang, Hyeong Soo | en_US |
| dc.contributor.author | Fu, Michael C. | en_US |
| dc.contributor.department | ISR | en_US |
| dc.date.accessioned | 2007-05-23T10:13:46Z | |
| dc.date.available | 2007-05-23T10:13:46Z | |
| dc.date.issued | 2003 | en_US |
| dc.description.abstract | We consider a class of infinite horizon Markov decision processes (MDPs) with multiple decision makers, called agents,and a general joint reward structure, but a special decomposable state/action structure such that each individual agent's actions affect the system's state transitions independently from the actions of all other agents. We introduce the concept of ``localization," where each agent need only consider a ``local" MDP defined on its own state and action spaces. Based on this localization concept, we propose an iterative distributed algorithm that emulates gradient ascent and which converges to a locally optimal solution for the average reward case. The solution is an ``autonomous" joint policy such that each agent's action is based on only its local state. Finally, we discuss the implication of the localization concept for discounted reward problems. | en_US |
| dc.format.extent | 195556 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1903/6357 | |
| dc.language.iso | en_US | en_US |
| dc.relation.ispartofseries | ISR; TR 2003-25 | en_US |
| dc.subject | NULL | en_US |
| dc.title | A Distributed Algorithm for Solving a Class of Multi-agent Markov Decision Problems | en_US |
| dc.type | Technical Report | en_US |
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