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… Click to show full abstract
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
               
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