Actor-Critic Algorithms

Part of Advances in Neural Information Processing Systems 12 (NIPS 1999)

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Vijay Konda, John Tsitsiklis


We propose and analyze a class of actor-critic algorithms for simulation-based optimization of a Markov decision process over a parameterized family of randomized stationary policies. These are two-time-scale algorithms in which the critic uses TD learning with a linear approximation architecture and the actor is updated in an approximate gradient direction based on information pro(cid:173) vided by the critic. We show that the features for the critic should span a subspace prescribed by the choice of parameterization of the actor. We conclude by discussing convergence properties and some open problems.