Transactions of the Institute of Systems, Control and Information Engineers
Online ISSN : 2185-811X
Print ISSN : 1342-5668
Reinforcement Learning Using Regularization Theory to Treat the Continuous States and Actions
Takanori FUKAONorikatsu INEYAMANorihiko ADACHI
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1998 Volume 11 Issue 11 Pages 593-599

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Abstract

Reinforcement learning is to learn how to act optimally in an unknown environment. It requires only a scalar reinforcement signal as performance feedback from the environment. Q-learning is one of the famous algorithms for the reinforcement learning. This paper presents a new method that is able to treat the continuous states and actions in the Q-learning. That is because a Q-function is smoothly approximated by using regularization theory.

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