The generalization of reinforcement learning methods to large-scale or continuous spaces has become a major focus in the research field of reinforcement learning.Unlike the present reinforcement learning methods for continuous spaces based on a value-function approximation method

the reinforcement learning is constructed as a simple binary-class problem.A kind of reinforcement learning method for continuous state and action spaces based on a Gaussian process classifier is proposed using a classification algorithm to obtain a control policy.At first

a continuous action space is discretized into discrete actions with definite number

and the Gaussian process classifier is used to predict the probability of class for a continuous-state-discrete-action pair.Then a continuous action is generated based on a weighted operation of the positive actions with their probability values.Computer simulations involving a boat problem illustrate the validity of the proposed reinforcement learning method.