JACIII Vol.20 No.6 pp. 983-991
doi: 10.20965/jaciii.2016.p0983


Visualization of Learning Process in “State and Action” Space Using Self-Organizing Maps

Akira Notsu*, Yuichi Hattori**, Seiki Ubukata*, and Katsuhiro Honda*

*Osaka Prefecture University
1-1 Gakuen-cho, Nakaku, Sakai, Osaka 599-8531, Japan

**IT Platform Service Division, Nomura Research Institute, Ltd.
1-6-5 Marunouchi, Chiyoda-ku, Tokyo 100-0005, Japan

February 19, 2016
August 24, 2016
November 20, 2016
self-organizing maps, reinforcement learning, visualization of learning process
In reinforcement learning, agents can learn appropriate actions for each situation based on the consequences of these actions after interacting with the environment. Reinforcement learning is compatible with self-organizing maps that accomplish unsupervised learning by reacting to impulses and strengthening neurons. Therefore, numerous studies have investigated the topic of reinforcement learning in which agents learn the state space using self-organizing maps. In this study, while we intended to apply these previous studies to transfer the learning and visualization of the human learning process, we introduced self-organizing maps into reinforcement learning and attempted to make their “state and action” learning process visible. We performed numerical experiments with the 2D goal-search problem; our model visualized the learning process of the agent.
Cite this article as:
A. Notsu, Y. Hattori, S. Ubukata, and K. Honda, “Visualization of Learning Process in “State and Action” Space Using Self-Organizing Maps,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.6, pp. 983-991, 2016.
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Last updated on May. 10, 2024