Accelerated Gradient Temporal Difference Learning

Yangchen Pan; Adam White; Martha White

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arXiv · 1611.09328

Accelerated Gradient Temporal Difference Learning

Yangchen Pan·Adam White·Martha White

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Abstract

The family of temporal difference (TD) methods span a spectrum from computationally frugal linear methods like TD({\lambda}) to data efficient least squares methods. Least square methods make the best use of available data directly computing the TD solution and thus do not require tuning a typically highly sensitive learning rate parameter, but require quadratic computation and storage. Recent algorithmic developments have yielded several sub-quadratic methods that use an approximation to the least squares TD solution, but incur bias. In this paper, we propose a new family of accelerated gradient TD (ATD) methods that (1) provide similar data efficiency benefits to least-squares methods, at a fraction of the computation and storage (2) significantly reduce parameter sensitivity compared to linear TD methods, and (3) are asymptotically unbiased. We illustrate these claims with a proof of convergence in expectation and experiments on several benchmark domains and a large-scale industrial energy allocation domain.

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Yangchen Pan, Adam White, Martha White. 2016-11-28. Accelerated Gradient Temporal Difference Learning. https://arxiv.org/abs/1611.09328

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