Presentation | 2000/7/12 Reinforcement Signal Communication based Multiagent Reinforcement Learning Tomohiro YAMAGUCHI, |
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Abstract(in Japanese) | (See Japanese page) |
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. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | reinforcement learning / multiagent / self-reflection / reinforcement signal / communication / interactive |
Paper # | OFS2000-29,AI2000-31 |
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Conference Information | |
Committee | AI |
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Conference Date | 2000/7/12(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Artificial Intelligence and Knowledge-Based Processing (AI) |
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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 |