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Coordinated reinforcement learning

Lagoudakis Michael, Guestrin, C., Parr, R.

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URIhttp://purl.tuc.gr/dl/dias/15CFBFB5-CCCD-4BC0-ABAF-DAAE65C69CBD-
Identifierhttp://www.cs.berkeley.edu/~russell/classes/cs294/f05/papers/guestrin+al-2002.pdf-
Languageen-
Extent8 pagesen
TitleCoordinated reinforcement learningen
CreatorLagoudakis Michaelen
CreatorΛαγουδακης Μιχαηλel
CreatorGuestrin, C.en
CreatorParr, R.en
Content SummaryWe present several new algorithms for multiagent reinforcement learning. A common feature of these algorithms is a parameterized, structured representation of a policy or value function. This structure is leveraged in an approach we call coordinated reinforcement learning, by which agents coordinate both their action selection activities and their parameter updates. Within the limits of our parametric representations, the agents will determine a jointly optimal action without explicitly considering every possible action in their exponentially large joint action space. Our methods differ from many previous reinforcement learning approaches to multiagent coordination in that structured communication and coordination between agents appears at the core of both the learning algorithm and the execution architecture. Our experimental results, comparing our approach to other RL methods, illustrate both the quality of the policies obtained and the additional benefits of coordination. en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-11-13-
Date of Publication2002-
SubjectReinforcement Learningen
Bibliographic CitationC. Guestrin, M. G. Lagoudakis. (2002, July).Coordinated reinforcement learning. [Online]. Available: http://www.cs.berkeley.edu/~russell/classes/cs294/f05/papers/guestrin+al-2002.pdf en

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