| Project/Area Number |
26330419
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Entertainment and game informatics 1
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| Research Institution | Shibaura Institute of Technology |
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Principal Investigator |
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| Co-Investigator(Renkei-kenkyūsha) |
ISHIHARA Seiji 東京電機大学, 理工学部, 准教授 (50351656)
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| Research Collaborator |
MORIOKA Yuichi
YAMAMOTO Kazumasa
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| Project Period (FY) |
2014-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2016: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2015: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2014: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
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| Keywords | 強化学習 / 方策勾配法 / マルチエージェント / コンピュータ将棋 / ロボカップ / ソフトマックス探索 / サッカー / マルチエージェントシステム / RoboCup / ファジィ推論 |
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| Outline of Final Research Achievements |
In this research project, we have made theoretical and practical research for developing expressions of policy functions and learning methods in the policy gradient reinforcement learning algorithms. Our final goal is constructing a general methodology that can be applied to computer games and engineering fields. The results of this project are as follows.
(1)Theoretical research on the policy gradient reinforcement learning: we proposed new methods in ①hierarchical reinforcement learning to learn higher strategies of agents, ②learning with separated knowledge of environmental dynamics and action-values in agent policies, and ③learning with a fuzzy controller for policies.
(2) Practical application of the policy gradient reinforcement learning: we applied the proposed learning methods to pursuit games, robot soccer games and computer shogi and examines the efficiency of our methods.
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