Mon, March 20, 2017, noon:
Dean Yang, Taken by Storm
Murphy, Susan A. 2005. "A generalization error for Q-learning." Journal of Machine Learning Research, 6(July): 1073-1097.
Planning problems that involve learning a policy from a single training set of finite horizon trajectories arise in both social science and medical fields. We consider Q-learning with function approximation for this setting and derive an upper bound on the generalization error. This upper bound is in terms of quantities minimized by a Q-learning algorithm, the complexity of the approximation space and an approximation term due to the mismatch between Q-learning and the goal of learning a policy that maximizes the value function.
PMCID: PMC1475741. (Pub Med Central)