  Several researchers have recently investigated the connection between
reinforcement learning and classification. We are motivated by proposals of
approximate policy iteration schemes without value functions which focus on
policy representation using classifiers and address policy learning as a
supervised learning problem. This paper proposes variants of an improved policy
iteration scheme which addresses the core sampling problem in evaluating a
policy through simulation as a multi-armed bandit machine. The resulting
algorithm offers comparable performance to the previous algorithm achieved,
however, with significantly less computational effort. An order of magnitude
improvement is demonstrated experimentally in two standard reinforcement
learning domains: inverted pendulum and mountain-car.
