1998 Volume 11 Issue 11 Pages 593-599
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.