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Emotional multiagent reinforcement learning in spatial social dilemmas

Journal Article


Abstract


  • Social dilemmas have attracted extensive interest in the research of multiagent systems in order to study the emergence of cooperative behaviors among selfish agents. Understanding how agents can achieve cooperation in social dilemmas through learning from local experience is a critical problem that has motivated researchers for decades. This paper investigates the possibility of exploiting emotions in agent learning in order to facilitate the emergence of cooperation in social dilemmas. In particular, the spatial version of social dilemmas is considered to study the impact of local interactions on the emergence of cooperation in the whole system. A doublelayered emotional multiagent reinforcement learning framework is proposed to endow agents with internal cognitive and emotional capabilities that can drive these agents to learn cooperative behaviors. Experimental results reveal that various network topologies and agent heterogeneities have significant impacts on agent learning behaviors in the proposed framework, and under certain circumstances, high levels of cooperation can be achieved among the agents.

Publication Date


  • 2015

Citation


  • Yu, C., Zhang, M., Ren, F. & Tan, G. (2015). Emotional multiagent reinforcement learning in spatial social dilemmas. IEEE Transactions on Neural Networks and Learning Systems, 26 (12), 3083-3096.

Scopus Eid


  • 2-s2.0-84958116980

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5223

Has Global Citation Frequency


Number Of Pages


  • 13

Start Page


  • 3083

End Page


  • 3096

Volume


  • 26

Issue


  • 12

Place Of Publication


  • United States

Abstract


  • Social dilemmas have attracted extensive interest in the research of multiagent systems in order to study the emergence of cooperative behaviors among selfish agents. Understanding how agents can achieve cooperation in social dilemmas through learning from local experience is a critical problem that has motivated researchers for decades. This paper investigates the possibility of exploiting emotions in agent learning in order to facilitate the emergence of cooperation in social dilemmas. In particular, the spatial version of social dilemmas is considered to study the impact of local interactions on the emergence of cooperation in the whole system. A doublelayered emotional multiagent reinforcement learning framework is proposed to endow agents with internal cognitive and emotional capabilities that can drive these agents to learn cooperative behaviors. Experimental results reveal that various network topologies and agent heterogeneities have significant impacts on agent learning behaviors in the proposed framework, and under certain circumstances, high levels of cooperation can be achieved among the agents.

Publication Date


  • 2015

Citation


  • Yu, C., Zhang, M., Ren, F. & Tan, G. (2015). Emotional multiagent reinforcement learning in spatial social dilemmas. IEEE Transactions on Neural Networks and Learning Systems, 26 (12), 3083-3096.

Scopus Eid


  • 2-s2.0-84958116980

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5223

Has Global Citation Frequency


Number Of Pages


  • 13

Start Page


  • 3083

End Page


  • 3096

Volume


  • 26

Issue


  • 12

Place Of Publication


  • United States