Regularization and feature selection in least squares temporal difference with gradient correction
Dazi Li, Luntong Li, Tianheng Song, Qibing Jin · 2016
We focus on the learning prediction problems in reinforcement learning with linear function approximation. In particular, the ℓ1-regularized problems in least-squares temporal difference with gradient correction (LS-TDC) are studied. Since LS-TDC contains gradient correction term, the convergence rate of LS-TDC is higher than that of least-squares temporal difference (LS-TD) algorithm. However, LS-TDC may over-fit to data as LS-TD does when the number of features is larger than that of samples. Thus, the regularization and feature selection of LS-TDC are studied. It is well known that ℓ1-regularization can produce sparse solutions and often serves as an automatic feature selection method in value function approximation. The ℓ1-regularized problem in LS-TDC adds a penalty term into the fixed-point function, but this augment function cannot be solved analytically. We turn to build the optimal solution incrementally by using an algorithm similar to Least Angle Regression (LARS) algorithm and LARS-TD algorithm. By using LARS algorithm, an ℓ1-regularized version of LS-TDC named LARS-TDC is proposed. Experiment results show that LARS-TDC is an effective method to solve the ℓ1-regularized problem.