Reinforcement Learning Using Regularization Theory to Treat the Continuous States and Actions
Takanori FUKAO, Norikatsu INEYAMA, Norihiko ADACHI
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.