Rollout sampling approximate policy iteration

doi:10.1007/s10994-008-5069-3

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Rollout sampling approximate policy iteration

2008-07-10

Christos Dimitrakakis *

Michail G. Lagoudakis

DOI：10.1007/s10994-008-5069-3

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Abstract

En 中文

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.

Keywords:

reinforcement learning

approximate policy iteration

rollouts

bandit problems

classification

sample complexity