A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms

27 Mar 2020Philip AmortilaDoina PrecupPrakash PanangadenMarc G. Bellemare

We present a distributional approach to theoretical analyses of reinforcement learning algorithms for constant step-sizes. We demonstrate its effectiveness by presenting simple and unified proofs of convergence for a variety of commonly-used methods... (read more)

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