2012 Fiscal Year Final Research Report
Theory of Reinforcement Learning and Algorithms of Route Choice in Transportation Networks
| Project/Area Number |
22360201
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Civil engineering project/Traffic engineering
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| Research Institution | Tohoku University |
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Principal Investigator |
MIYAGI Toshihiko 東北大学, 大学院・情報科学研究科, 教授 (20092968)
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| Co-Investigator(Kenkyū-buntansha) |
FUKUMOTO Jyunya 東北大学, 大学院・情報科学研究科, 准教授 (30323447)
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| Project Period (FY) |
2010 – 2012
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| Keywords | 繰り返しゲーム / 強化学習 / 交通行動理論 / 適応学習アルゴリズム / Nash 均 衡 / 利用者均衡確率近似理論 / 動的離散的選択モデル |
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| Research Abstract |
This research shows that an individual traveler in transportation networks is rigorously modeled as an adaptive learning agent who receives travel information through day-to-day experience and makes his decision so as to reinforce his action depending the realized payoffs. An adaptive learning algorithm consistent with the theory is proposed and proved that it leads the system to a Nash equilibrium with probability one. The proposed algorithms have tested numerically by using example networks with various ill-defined link cost functions and examined a rapid convergence of the algorithms. In addition, we have proposed an estimation method for the structure parameters included in the route choice model. The application to the data of theday-to-day route choice obtained by the indoor experiments was satisfactory.
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