Reinforcement learning algorithm with CTRNN in continuous action space

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

Reinforcement learning algorithm with CTRNN in continuous action space

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

School of Creative Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings
Publisher	Springer Verlag
Pages	387-396
Number of pages	10
ISBN (Print)	3540464794, 9783540464792
Publication status	Published - 2006 Jan 1
Event	13th International Conference on Neural Information Processing, ICONIP 2006 - Hong Kong, China Duration: 2006 Oct 3 → 2006 Oct 6

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4232 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Neural Information Processing, ICONIP 2006
Country	China
City	Hong Kong
Period	06/10/3 → 06/10/6

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

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Arie, H., Namikawa, J., Ogata, T., Tani, J., & Sugano, S. (2006). Reinforcement learning algorithm with CTRNN in continuous action space. In Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings (pp. 387-396). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4232 LNCS). Springer Verlag.

Reinforcement learning algorithm with CTRNN in continuous action space. / Arie, Hiroaki; Namikawa, Jun; Ogata, Tetsuya; Tani, Jun; Sugano, Shigeki.

Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings. Springer Verlag, 2006. p. 387-396 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4232 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Arie, H, Namikawa, J, Ogata, T, Tani, J & Sugano, S 2006, Reinforcement learning algorithm with CTRNN in continuous action space. in Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4232 LNCS, Springer Verlag, pp. 387-396, 13th International Conference on Neural Information Processing, ICONIP 2006, Hong Kong, China, 06/10/3.

Arie H, Namikawa J, Ogata T, Tani J, Sugano S. Reinforcement learning algorithm with CTRNN in continuous action space. In Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings. Springer Verlag. 2006. p. 387-396. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Arie, Hiroaki ; Namikawa, Jun ; Ogata, Tetsuya ; Tani, Jun ; Sugano, Shigeki. / Reinforcement learning algorithm with CTRNN in continuous action space. Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings. Springer Verlag, 2006. pp. 387-396 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "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.",

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AB - 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.

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Reinforcement learning algorithm with CTRNN in continuous action space

Abstract

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Conference

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