Proximal Gradient Temporal Difference Learning Algorithms - IJCAI
TD algorithms with linear function approximation are shown to be convergent when the samples are generated from the target policy (known as on-policy prediction) ...
TD(?) and the Proximal Algorithm - MITIt yields a value function, the quality assessment of states for a given policy, which can be used in a policy improvement step. Since the late 1980s, this ... A Concave-Convex Procedure for TDOA Based PositioningVariance reduction techniques have been successfully applied to temporal- difference (TD) learning and help to improve the sample complexity in policy. A Convergent Off-Policy Temporal Difference Algorithm - Ecai 2020In this paper, we provide the finite-sample anal- ysis of the GTD family of algorithms, a relatively novel class of gradient-based TD methods that are ...
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