Presentation 1996/5/24
Emergent Organization of Coordinated Behavior by Modular Reinforcement-Learning Agents
Kenji FUKUMOTO, Osamu IKEDA, Norihiko ONO,
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Abstract(in English) Recently several attempts have been reported to let multiple monolithic reinforcement-learning agents synthesize coordinated decision policies needed to accomplish their common goals effectively. Most of these 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. In this paper, taking the Pursuit Problem as such a learning problem that is computationally intractable by these straightforward approaches, we show how successfully a collection of modular Q-learning pursuer agents synthesize coordinated decision policies needed to capture a randomly-fleeing fugitive agent effectively, by specializing their individual functionality and organizing herding behavior.
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Keyword(in English) multi-agent systems / machine learning / reinforcement-learning
Paper # AI96-5
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Committee AI
Conference Date 1996/5/24(1days)
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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 Coordinated Behavior by Modular Reinforcement-Learning Agents
Sub Title (in English)
Keyword(1) multi-agent systems
Keyword(2) machine learning
Keyword(3) reinforcement-learning
1st Author's Name Kenji FUKUMOTO
1st Author's Affiliation Faculty of Engineering, University of Tokushima()
2nd Author's Name Osamu IKEDA
2nd Author's Affiliation Faculty of Engineering, University of Tokushima
3rd Author's Name Norihiko ONO
3rd Author's Affiliation Faculty of Engineering, University of Tokushima
Date 1996/5/24
Paper # AI96-5
Volume (vol) vol.96
Number (no) 77
Page pp.pp.-
#Pages 6
Date of Issue