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Actor-Critic--Type Learning Algorithms for Markov Decision Processes | Synapse

January 1, 1999

Actor-Critic--Type Learning Algorithms for Markov Decision Processes

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Abstract

Algorithms for learning the optimal policy of a Markov decision process (MDP) based on simulated transitions are formulated and analyzed. These are variants of the well-known "actor-critic" (or "adaptive critic") algorithm in the artificial intelligence literature. Distributed asynchronous implementations are considered. The analysis involves two time scale stochastic approximations.

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Konda et al. (Fri,) studied this question.

synapsesocial.com/papers/6a155b12d73ae7522a4e2a9a https://doi.org/https://doi.org/10.1137/s036301299731669x

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