Abstract: In order to solve the problem of "curse of dimensionality" , which means that the states space will grow exponentially in the number of features, in large discrete states space in reinforcement lcarning,a reinforcement learning method based on Gaussian processes was proposed. The Gaussian processes model can represent the distribution of functions,and it can be used to get a distribution of the expectation instead of its value. The experiment result shows that the performance such as speed of convergence and final effect can be improved obviously with the reinforcement learning method combined Gaussian processes. The "curse of dimensionality" in large discrete states space could be solved to a certain extent with the Gaussian processes regression model.

Key words: Reinforcement learning,Curse of dimensionality,Uaussian processes,Regression,Distribution of functions