Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolutionary Function Approximation for Reinforcement Learning
The Journal of Machine Learning Research
Experiments with the n-tuple Method of Pattern Recognition
IEEE Transactions on Computers
A theoretical and experimental account of n-tuple classifier performance
Neural Computation
Pattern recognition and reading by machine
IRE-AIEE-ACM '59 (Eastern) Papers presented at the December 1-3, 1959, eastern joint IRE-AIEE-ACM computer conference
Evolving an expert checkers playing program without using humanexpertise
IEEE Transactions on Evolutionary Computation
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This paper introduces a novel function approximation architecture especially well suited to temporal difference learning. The architecture is based on using sets of interpolated table look-up functions. These offer rapid and stable learning, and are efficient when the number of inputs is small. An empirical investigation is conducted to test their performance on a supervised learning task, and on the mountain car problem, a standard reinforcement learning benchmark. In each case, the interpolated table functions offer competitive performance.