Regularized Off-Policy TD-Learning

author: Bo Liu, Department of Computer Science, University of Massachusetts Amherst
published: Jan. 14, 2013,   recorded: December 2012,   views: 99
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Description

We present a novel l1 regularized off-policy convergent TD-learning method (termed RO-TD), which is able to learn sparse representations of value functions with low computational complexity. The algorithmic framework underlying RO-TD integrates two key ideas: off-policy convergent gradient TD methods, such as TDC, and a convex-concave saddle-point formulation of non-smooth convex optimization, which enables first-order solvers and feature selection using online convex regularization. A detailed theoretical and experimental analysis of RO-TD is presented. A variety of experiments are presented to illustrate the off-policy convergence, sparse feature selection capability and low computational cost of the RO-TD algorithm.

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Download slides icon Download slides: machine_liu_learning_01.pdf (422.9 KB)

Download article icon Download article: machine_liu_learning_01.pdf (198.9 KB)


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