Presentation 2000/7/12
Reinforcement Signal Communication based Multiagent Reinforcement Learning
Tomohiro YAMAGUCHI,
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Abstract(in English) Reinforcement learning is the major learning mechanism for an agent to adapt itself to various situations flexibly.However, in a multiagent system environment that has mutual dependency among agents, it is difficult for a human to setup suitable learning goals for each agent.Therefore, it requires the active and interactive learning function that treats how to coordinate the interaction among other learning agents.This paper presents a new framework of multiagent reinforcement learning to generate and coordinate each learning goal interactively among agents.To realize this, it presents to treat each learning goal as a reinforcement signal that can be communicated among agents.Then the issues of the self-generation of goals and evaluation criteria are discussed.
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Keyword(in English) reinforcement learning / multiagent / self-reflection / reinforcement signal / communication / interactive
Paper # OFS2000-29,AI2000-31
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Committee AI
Conference Date 2000/7/12(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) Reinforcement Signal Communication based Multiagent Reinforcement Learning
Sub Title (in English)
Keyword(1) reinforcement learning
Keyword(2) multiagent
Keyword(3) self-reflection
Keyword(4) reinforcement signal
Keyword(5) communication
Keyword(6) interactive
1st Author's Name Tomohiro YAMAGUCHI
1st Author's Affiliation Department of Information Engineering, Nara National College of Technology()
Date 2000/7/12
Paper # OFS2000-29,AI2000-31
Volume (vol) vol.100
Number (no) 199
Page pp.pp.-
#Pages 8
Date of Issue