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Performance comparison of non-RNN and RNN in Emergence of Discrete Decision Making through Reinforcement Learning
Mohamad Faizal Samsudin, Katsunari Shibata
20 - 27
10.3233/978-1-61499-105-2-20
Frontiers in Artificial Intelligence and Applications
Volume 243: Advances in Knowledge-Based and Intelligent Information and Engineering Systems
Abstract

Using a neural network in the task that requires discrete decision making suffers from the problem of discrete decision making. On the other hand, using a lookup table suffers from the problem in generalization and the curse of dimensionality. In this paper, simple localized inputs in neural network are used in order to overcome this problem. Furthermore, by utilizing the internal dynamics in RNN, it is expected that quick discrete decision making can be obtained through learning.

$35.00 / €27.50 / £22.00
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