1.3k citations · 2.6k across the 20 of their papers we have counts for
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cs.AI2011★ 139 cited
Experiments with Infinite-Horizon, Policy-Gradient Estimation
J. Baxter, P. L. Bartlett, L. Weaver
In this paper, we present algorithms that perform gradient ascent of the average reward in a partially observable Markov decision process (POMDP). These algorithms are based on GPO…
cs.AI2011★ 343 cited
Infinite-Horizon Policy-Gradient Estimation
Jonathan Baxter, Peter L. Bartlett
Gradient-based approaches to direct policy search in reinforcement learning have received much recent attention as a means to solve problems of partial observability and to avoid s…
cs.AI2011★ 573 cited
A Model of Inductive Bias Learning
J. Baxter
A major problem in machine learning is that of inductive bias: how to choose a learner's hypothesis space so that it is large enough to contain a solution to the problem being lear…