Reinforcement social learning of coordination in cooperative multiagent systems
Refereed conference paper presented and published in conference proceedings


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AbstractCoordination in cooperative multiagent systems is an important problem and has received a lot of attention in multiagent learning literature. Most of previous works study the problem of how two (or more) players can coordinate on Pareto-optimal Nash equilibrium(s) through fixed and repeated interactions in the context of cooperative games. However, in practical complex environments, the interactions between agents can be sparse, and each agent's interacting partners may change frequently and randomly. To this end, in this paper, we investigate the multiagent coordination problems in cooperative environments under the social learning framework, in which there exists a large population of agents and each agent interacts with another agent randomly in each round. Each agent learns its policy through repeated interactions with the rest of agents via social learning. We distinguish two different types of learners depending on the amount of information each agent can perceive: individual action learner and joint action learner. The learning performance of both types of learners are evaluated under a number of challenging deterministic and stochastic cooperative games.
All Author(s) ListHao J., Leung H.-F.
Name of Conference12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013
Start Date of Conference06/05/2013
End Date of Conference10/05/2013
Place of ConferenceSaint Paul, MN
Country/Region of ConferenceUnited States of America
Detailed descriptionorganized by IFAAMAS,
Year2013
Month1
Day1
Volume Number2
Pages1321 - 1322
LanguagesEnglish-United Kingdom
KeywordsCooperative games, Coordination, Multiagent learning

Last updated on 2020-02-09 at 00:55