DISCUSSION PAPER SERIES - CREST
Many RL algorithms, especially those that are based on stochastic approximation, such as. TD(?), do not have convergence guarantees in the off-policy setting.
Finite Sample Analysis of the GTD Policy Evaluation Algorithms in ...In this section, we present the works for studying TD learning and the recent advances in achieving DP in RL. Temporal Difference Learning ... Proximal Gradient Temporal Difference Learning Algorithms - IJCAITD 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 ...
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