Reinforcement Learning Using Regularization Theory to Treat the Continuous States and Actions
Takanori FUKAO, Norikatsu INEYAMA, Norihiko ADACHI
pp. 593-599
DOI:
10.5687/iscie.11.593Abstract
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.