Behavior Acquisition on a Mobile Robot Using Reinforcement Learning with Continuous State Space

Tomoyuki Arai; Yuichiro Toda; Naoyuki Kubota

doi:10.1109/ICMLC48188.2019.8949181

Behavior Acquisition on a Mobile Robot Using Reinforcement Learning with Continuous State Space

Tomoyuki Arai, Yuichiro Toda, Naoyuki Kubota

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

Abstract

In the application of Reinforcement Learning to real tasks, the construction of state space is a significant problem. In order to use in the real-world environment, we need to deal with the problem of continuous information. Therefore, we proposed a method of the construction of state space using Growing Neural Gas. In our method, the agent constructs a state space model from its own experience autonomously. Furthermore, it can reconstruct the suitable state space model to adapt the complication of the environment. Through the experiments, we showed that Reinforcement Learning could be performed efficiently by successively updating the state space model according to the environment.

Original language	English
Title of host publication	Proceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019
Publisher	IEEE Computer Society
ISBN (Electronic)	9781728128160
DOIs	https://doi.org/10.1109/ICMLC48188.2019.8949181
Publication status	Published - Jul 2019
Event	18th International Conference on Machine Learning and Cybernetics, ICMLC 2019 - Kobe, Japan Duration: Jul 7 2019 → Jul 10 2019

Publication series

Name	Proceedings - International Conference on Machine Learning and Cybernetics
Volume	2019-July
ISSN (Print)	2160-133X
ISSN (Electronic)	2160-1348

Conference

Conference	18th International Conference on Machine Learning and Cybernetics, ICMLC 2019
Country/Territory	Japan
City	Kobe
Period	7/7/19 → 7/10/19

Keywords

Machine learning
Mobile robot
Reinforcement learning
Self-Organization
State space

ASJC Scopus subject areas

Artificial Intelligence
Computational Theory and Mathematics
Computer Networks and Communications
Human-Computer Interaction

Access to Document

10.1109/ICMLC48188.2019.8949181

Cite this

Arai, T., Toda, Y., & Kubota, N. (2019). Behavior Acquisition on a Mobile Robot Using Reinforcement Learning with Continuous State Space. In Proceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019 [8949181] (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 2019-July). IEEE Computer Society. https://doi.org/10.1109/ICMLC48188.2019.8949181

Behavior Acquisition on a Mobile Robot Using Reinforcement Learning with Continuous State Space. / Arai, Tomoyuki; Toda, Yuichiro; Kubota, Naoyuki.

Proceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019. IEEE Computer Society, 2019. 8949181 (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 2019-July).

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

Arai, T, Toda, Y & Kubota, N 2019, Behavior Acquisition on a Mobile Robot Using Reinforcement Learning with Continuous State Space. in Proceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019., 8949181, Proceedings - International Conference on Machine Learning and Cybernetics, vol. 2019-July, IEEE Computer Society, 18th International Conference on Machine Learning and Cybernetics, ICMLC 2019, Kobe, Japan, 7/7/19. https://doi.org/10.1109/ICMLC48188.2019.8949181

Arai T, Toda Y, Kubota N. Behavior Acquisition on a Mobile Robot Using Reinforcement Learning with Continuous State Space. In Proceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019. IEEE Computer Society. 2019. 8949181. (Proceedings - International Conference on Machine Learning and Cybernetics). https://doi.org/10.1109/ICMLC48188.2019.8949181

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Behavior Acquisition on a Mobile Robot Using Reinforcement Learning with Continuous State Space

Abstract

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Keywords

ASJC Scopus subject areas

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