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Lecture Notes in Computer Science
In large and continuous state-action spaces reinforcement learning heavily relies on function approximation techniques. Tile coding is a well-known function approximator that has been successfully applied to many reinforcement learning tasks. In this paper we introduce the hyperplane tile coding, in which the usual tiles are replaced by parameterized hyperplanes that approximate the action-value function. We compared the performance of hyperplane tile coding with the usual tile coding on threedoi:10.1007/978-3-540-89722-4_14 fatcat:n6f7h5bbrzhnzee4itpa5wuxjq