Preconditioned Temporal Difference Learning

author: Hengshuai Yao, School of Creative Media, City University of Hong Kong
published: Aug. 12, 2008,   recorded: July 2008,   views: 3268


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This paper extends many of the recent popular reinforcement learning (RL) algorithms to a generalized framework that includes least-squares temporal difference (LSTD) learning, least-squares policy evaluation (LSPE) and a variant of incremental LSTD (iLSTD). The basis of this extension is a preconditioning technique that tries to solve a stochastic model equation. This paper also studies three signicant issues of the new framework: it presents a new rule of step-size that can be computed online, provides an iterative way to apply preconditioning, and reduces the complexity of related algorithms to near that of temporal difference (TD) learning.

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