Q Learning Based on Probability Support Vector Classification Machine
CHENG Yu-hu
GAO Yang
WANG Xue-song
Abstract：The state-action space of a Q learning system was divided into positive and negative classes according to TD error criterion. In order to describe the uncertainty of classification and to solve the problem of low learning precision resulted from simple classification, a probability support vector classification machine (PSVCM) was used to make the classification of samples both have qualitative explanation and quantitative evaluation. The inputs of PSVCM are continuous states and discrete actions, while its output is a class label with a probability value. A Q learning control strategy for continuous action space can be obtained based on a weighted operation of the positive actions with their probability values. The simulations results of a boat problem show that the proposed method is suitable for Q learning control for nonlinear systems with continuous states and continuous actions compared with Q learning based on traditional SVCM and the control performance is robust with respect to the setting of initial action.
Keywords：probabilitysupport vector classification machineTD errorQ learning
Publication Date：2010-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)