Online Sparse Temporal Difference Learning Based on Nested Optimization and Regularized Dual Averaging

In policy evaluation of reinforcement learning tasks, the temporal difference (TD) learning with value function approximation has been widely studied. However, feature representation has a decisive influence on both accuracy of value function approximation and convergence rate. Therefore, it is important to develop the feature selection theory and methods that can efficiently prevent overfitting and improve estimation accuracy in TD learning algorithms. In this article, we propose an online sparse TD learning algorithm for policy evaluation by using$\ell _{1}$-regualrization for feature selection. The per-step-time runtime computational complexity of the proposed algorithm is linear with respect to feature dimension. The loss function is defined as a nested optimization with$\ell _{1}$-regularization penalty, and the solver minimizes two suboptimization problems by running stochastic gradient descent and regularized dual averaging method, alternately. The convergence results for the fixed points are also established. The experiments on benchmarks with high-dimensional features show the abilities of learning and generalization of the proposed algorithms.