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Summary & Details

Solving the Deadlock Problem with Deep Reinforcement Learning to Consider Information from Multiple Vehicles

複数車両の情報を考慮する深層強化学習を用いたデッドロック問題の解決

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Format: PDF

Price: Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880

Publication code: 20216134

Paper/Info type: Proceedings (Autumn)

No.110-21

Pages: 1-6(Total 6 p)

Date of publication: Oct 2021

Publisher: JSAE

Language: Japanese

Event: 2021 JSAE Annual Congress (Autumn) [Online]

Detailed Information

Author(J)	1) 五藤強志, 2) 山下隆義, 3) 藤吉弘亘, 4) 平川翼
Author(E)	1) Tsuyoshi Goto, 2) Takayoshi Yamashita, 3) Hironobu Fujiyoshi, 4) Tsubasa Hirakawa
Affiliation(J)	1) 中部大学, 2) 中部大学, 3) 中部大学, 4) 中部大学
Affiliation(E)	1) Chubu University, 2) Chubu University, 3) Chubu University, 4) Chubu University
Abstract(J)	自動運転において，デッドロック問題により，衝突が起きてしまうようなケースが発生する．そのため，問題を解決する機構が必要不可欠である．本研究では，複数車両の情報を考慮しつつ学習を行うマルチエージェント強化学習により問題を解決する．実際にデッドロック問題が発生した事故場面を再現し，提案手法の効果を示す． Translation
Abstract(E)	In automatic driving, collisions may happen with each other because of deadlock problem. Therefore, a mechanism to solve the problem is essential. In this paper, we solve the deadlock problem by deep reinforcement learning to consider information from multiple vehicles. In experiment, we build the environment that happens the deadlock problem and demonstrate the effectiveness of the proposed method.

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