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The Explainable Model to Multi-Objective Reinforcement Learning Toward an Autonomous Smart System

The Explainable Model to Multi-Objective Reinforcement Learning Toward an Autonomous Smart System

Tomohiro Yamaguchi
Copyright: © 2023 |Pages: 17
ISBN13: 9781668476840|ISBN10: 1668476843|ISBN13 Softcover: 9781668476857|EISBN13: 9781668476864
DOI: 10.4018/978-1-6684-7684-0.ch002
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MLA

Yamaguchi, Tomohiro. "The Explainable Model to Multi-Objective Reinforcement Learning Toward an Autonomous Smart System." Perspectives and Considerations on the Evolution of Smart Systems, edited by Maki K. Habib, IGI Global, 2023, pp. 18-34. https://doi.org/10.4018/978-1-6684-7684-0.ch002

APA

Yamaguchi, T. (2023). The Explainable Model to Multi-Objective Reinforcement Learning Toward an Autonomous Smart System. In M. Habib (Ed.), Perspectives and Considerations on the Evolution of Smart Systems (pp. 18-34). IGI Global. https://doi.org/10.4018/978-1-6684-7684-0.ch002

Chicago

Yamaguchi, Tomohiro. "The Explainable Model to Multi-Objective Reinforcement Learning Toward an Autonomous Smart System." In Perspectives and Considerations on the Evolution of Smart Systems, edited by Maki K. Habib, 18-34. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-7684-0.ch002

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Abstract

The mission of this chapter is to add an explainable model to multi-goal reinforcement learning toward an autonomous smart system to design both complex behaviors and complex decision making friendly for a human user. At the front of the introduction section, and a relation between reinforcement learning including an explainable model and a smart system is described. To realize the explainable model, this chapter formalizes the exploration of various behaviors toward sub-goal states efficiently and in a systematic way in order to collect complex behaviors from a start state towards the main goal state. However, it incurs significant learning costs in previous learning methods, such as behavior cloning. Therefore, this chapter proposes a novel multi-goal reinforcement learning method based on the iterative loop-action selection strategy. As a result, the complex behavior sequence is learned with a given sub-goal sequence as a sequence of macro actions. This chapter reports the preliminary work carried out under the OpenAIGym learning environment with the CartPoleSwingUp task.

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