Application of instruction-based behavior explanation to a reinforcement learning agent with changing policy

Yosuke Fukuchi, Masahiko Osawa, Hiroshi Yamakawa, Michita Imai

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

Agents that acquire their own policies autonomously have the risk of accidents caused by the agents’ unexpected behavior. Therefore, it is necessary to improve the predictability of the agents’ behavior in order to ensure the safety. Instruction-based Behavior Explanation (IBE) is a method for a reinforcement learning agent to announce the agent’s future behavior. However, it was not verified that the IBE was applicable to an agent that changes the policy dynamically. In this paper, we consider agents under training and improve the IBE for the application to agents with changing policy. We conducted an experiment to verify if the behavior explanation model of an immature agent worked even after the agent’s further training. The results indicated the applicability of the improved IBE to agents under training.

本文言語English
ホスト出版物のタイトルNeural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
出版社Springer Verlag
ページ100-108
ページ数9
10634 LNCS
ISBN(印刷版)9783319700861
DOI
出版ステータスPublished - 2017 1月 1
イベント24th International Conference on Neural Information Processing, ICONIP 2017 - Guangzhou, China
継続期間: 2017 11月 142017 11月 18

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
10634 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other24th International Conference on Neural Information Processing, ICONIP 2017
国/地域China
CityGuangzhou
Period17/11/1417/11/18

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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