Journal of Advanced Computational Intelligence and Intelligent Informatics
Online ISSN : 1883-8014
Print ISSN : 1343-0130
ISSN-L : 1883-8014
Regular Papers
Visualization of Learning Process in “State and Action” Space Using Self-Organizing Maps
Akira NotsuYuichi HattoriSeiki UbukataKatsuhiro Honda
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JOURNAL OPEN ACCESS

2016 Volume 20 Issue 6 Pages 983-991

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Abstract

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.

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