Towards generating simulated walking motion using position based deep reinforcement learning

William Jones, Siddhant Gangapurwala, Ioannis Havoutis, Kazuya Yoshida

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

Much of robotics research aims to develop control solutions that exploit the machine's dynamics in order to achieve an extraordinarily agile behaviour [1]. This, however, is limited by the use of traditional model-based control techniques such as model predictive control and quadratic programming. These solutions are often based on simplified mechanical models which result in mechanically constrained and inefficient behaviour, thereby limiting the agility of the robotic system in development [2]. Treating the control of robotic systems as a reinforcement learning (RL) problem enables the use of model-free algorithms that attempt to learn a policy which maximizes the expected future (discounted) reward without inferring the effects of an executed action on the environment.

本文言語English
ホスト出版物のタイトルTowards Autonomous Robotic Systems - 20th Annual Conference, TAROS 2019, Proceedings
編集者Kaspar Althoefer, Jelizaveta Konstantinova, Ketao Zhang
出版社Springer Verlag
ページ467-470
ページ数4
ISBN(印刷版)9783030253318
DOI
出版ステータスPublished - 2019
イベント20th Towards Autonomous Robotic Systems Conference, TAROS 2019 - London, United Kingdom
継続期間: 2019 7月 32019 7月 5

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)
11650 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference20th Towards Autonomous Robotic Systems Conference, TAROS 2019
国/地域United Kingdom
CityLondon
Period19/7/319/7/5

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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