Exponentiated Gradient Methods for Reinforcement Learning
This paper introduces and evaluates a natural extension of linear exponentiated gradient methods that makes them applicable to reinforcement learning problems. Just as these methods speed up supervised learning, we nd that they can also increase the ef-ciency of reinforcement learning. Comparisons are made with conventional reinforcement learning methods on two test problems using CMAC function approximators and replacing traces. On a small prediction task, exponentiated gradient methods showed no improvement, but on a larger control task (Mountain Car) they improved the learning speed by approximately 25%. A more detailed analysis suggests that the diierence may be due to the distribution of irrelevant features.