Temporal-Difference learning an online support vector regression approach

This paper proposes a new algorithm for Temporal-Difference (TD) learning using online support vector regression. It benefits from the good generalization properties support vector regression (SVR) has, and also can do incremental learning and automatically track variation of environment with time-varying characteristics. Using the online SVR we can obtain good estimation of value function in TD learning in linear and nonlinear prediction problems. Experimental results demonstrate the effectiveness of the proposed method by comparison with others methods.