title: A Generalization Error for Q-Learning.
abstract: 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.
authors.affiliation: Department of Statistics, University of Michigan, Ann Arbor, MI 48109-1107, USA.
authors.forename: Susan A
authors.initials: SA
authors.lastname: Murphy