Empirical Comparison of Gradient Descent andExponentiated Gradient Descent in
نویسندگان
چکیده
This report describes a series of results using the exponentiated gradient descent (EG) method recently proposed by Kivinen and Warmuth. Prior work is extended by comparing speed of learning on a nonstationary problem and on an extension to backpropagation networks. Most signi cantly, we present an extension of the EG method to temporal-di erence and reinforcement learning. This extension is compared to conventional reinforcement learning methods on two test problems using CMAC function approximators and replace traces. On the larger of the two problems, the average loss was approximately 25% smaller for the EG method. The relative computational complexity and parameter sensitivity of the two methods is also discussed.
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