Theory and Applications of Exploitation-oriented Learning XoL in Multi-agent Systems
Project/Area Number |
26330267
|
Research Category |
Grant-in-Aid for Scientific Research (C)
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Allocation Type | Multi-year Fund |
Section | 一般 |
Research Field |
Intelligent informatics
|
Research Institution | National Institution for Academic Degrees and Quality Enhancement of Higher Education |
Principal Investigator |
Miyazaki Kazuteru 独立行政法人大学改革支援・学位授与機構, 研究開発部, 准教授 (20282866)
|
Project Period (FY) |
2014-04-01 – 2017-03-31
|
Project Status |
Completed (Fiscal Year 2016)
|
Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2014: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
|
Keywords | マルチエージェントシステム / 経験強化型学習 / 強化学習 / 機械学習 / 人工知能 / ソフトコンピューティング / 深層学習 |
Outline of Final Research Achievements |
This research has achieved several progresses about theory and applications of Exploitation-oriented Learning XoL in multi-agent learning. In multi-agent learning, it is important to avoid the concurrent learning problem that occurs when multiple agents learn simultaneously. Firstly, we have proposed a method to avoid the problem. Secondly, we have focused on a positive effect of an indirect reward which is given to the agent that does not receive a reward directly. Especially, we have proposed a method to reduce the perceptual aliasing problem caused by imperfect perception. We have also described the relationship between our previous multi-agent learning theorem and the positive effect. Lastly, we have extended application areas to show the effectiveness of XoL in multi-agent learning through experiments to Keepaway tasks like soccer games. We believe that these results contribute to claim that XoL surpasses traditional reinforcement learning methods in multi-agent learning.
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Report
(4 results)
Research Products
(22 results)