Presentation	1995/11/16 Emergent Organization of Communication, Specialization, and Herding-Behavior among Reinforcement-Learning Agents Norihiko Ono,
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Abstract(in English)	To investigate the learning capabilities of reinforcement-learning agents under multi-agent environments, we have attempted to let multiple reinforcement-learning agents synthesize coordinated decision policies needed to accomplish their common goals effectively. So long as a small number of agents are engaged in their joint tasks and accordingly the state space for each agent is relatively small, even monolithic reinforcement-learning agents have successfully organized some interesting coordinated behavior. Most of such straightforward reinforcement-learning approaches, however, scale poorly to more complex multi-agent learning problems, because the state space for each learning agent grows exponentially in the number of its partner agents engaged in the joint task. This paper presents a modular reinforcement-learning approach to this kind of multi-agent learning problems and demonstrates its effective learning capabilities.
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Keyword(in English)	multi-agent systems / machine learning / reinforcement-learning
Paper #	AI95-39,PRU95-154
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Conference Information
Committee	AI
Conference Date	1995/11/16(1days)
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Paper Information
Registration To	Artificial Intelligence and Knowledge-Based Processing (AI)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Emergent Organization of Communication, Specialization, and Herding-Behavior among Reinforcement-Learning Agents
Sub Title (in English)
Keyword(1)	multi-agent systems
Keyword(2)	machine learning
Keyword(3)	reinforcement-learning
1st Author's Name	Norihiko Ono
1st Author's Affiliation	Faculty of Engineering, University of Tokushima()
Date	1995/11/16
Paper #	AI95-39,PRU95-154
Volume (vol)	vol.95
Number (no)	363
Page	pp.pp.-
#Pages	8
Date of Issue