PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Convergent Single Time Scale Actor Critic Algorithm
Dotan Di Castro and Ron Meir
A Convergent Single Time Scale Actor Critic Algorithm Volume NA, 2008.

Abstract

Actor-Critic based approaches were among the rst to address reinforcement learning in a general setting. Recently, these algorithms have gained renewed interest due to their generality, good convergence properties, and possible biological relevance. In this paper, we introduce an episodic temporal dierence actor-critic algorithm which is proved to converge to a neighborhood of a local maximum of the average reward. Linear function approximation is used by the critic in order estimate the value function, and the temporal dierence signal, which is passed from the critic to the actor. The main distinguishing feature of the present convergence proof is that both the actor and the critic operate on a similar time scale, while in most current convergence proofs they are required to have very dierent time scales in order to converge. Moreover, the same temporal dierence signal is used to update the parameters of both the actor and the critic. A limitation of the proposed approach, compared to results available for two time scale convergence, is that convergence is guaranteed only to a neighborhood of an optimal value, rather to an optimal value itself. The single time scale and identical temporal dierence signal used by the actor and the critic, may provide a step towards constructing more biologically realistic models of reinforcement learning in the brain.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:5267
Deposited By:Ron Meir
Deposited On:24 March 2009