Reinforcement learning algorithm with CTRNN in continuous action space

Hiroaki Arie, Jun Namikawa, Tetsuya Ogata, Jun Tani, Shigeki Sugano

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

There are some difficulties in applying traditional reinforcement learning algorithms to motion control tasks of robot. Because most algorithms are concerned with discrete actions and based on the assumption of complete observability of the state. This paper deals with these two problems by combining the reinforcement learning algorithm and CTRNN learning algorithm. We carried out an experiment on the pendulum swing-up task without rotational speed information. It is shown that the information about the rotational speed, which is considered as a hidden state, is estimated and encoded on the activation of a context neuron. As a result, this task is accomplished in several hundred trials using the proposed algorithm.

本文言語English
ホスト出版物のタイトルNeural Information Processing - 13th International Conference, ICONIP 2006, Proceedings
出版社Springer Verlag
ページ387-396
ページ数10
ISBN(印刷版)3540464794, 9783540464792
出版ステータスPublished - 2006 1 1
イベント13th International Conference on Neural Information Processing, ICONIP 2006 - Hong Kong, China
継続期間: 2006 10 32006 10 6

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)
4232 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference13th International Conference on Neural Information Processing, ICONIP 2006
CountryChina
CityHong Kong
Period06/10/306/10/6

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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