Technical Note: \cal Q-Learning
Machine Learning
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Evolvable hardware chips for industrial applications
Communications of the ACM
RoboCop: today and tomorrow-what we have learned
Artificial Intelligence - Special issue on Robocop: the first step
An evolvable hardware chip and its application as a multi-function prosthetic hand controller
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolution of homing navigation in a real mobile robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Implementation of a Gate-Level Evolvable Hardware Chip
ICES '01 Proceedings of the 4th International Conference on Evolvable Systems: From Biology to Hardware
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This paper presents an integrated on-line learning system to evolve programmable logic array (PLA) controllers for navigating an autonomous robot in a two-dimensional environment. The integrated online learning system consists of two learning modules: one is the module of reinforcement learning based on temporal-difference learning methods, and the other is the module of evolutionary learning based on genetic algorithms. The control rules extracted from the module of reinforcement learning can be used as input to the module of evolutionary learning, and quickly implemented by the PLA through on-line evolution. The on-line evolution has shown promise as a method of learning systems in complex environment. The evolved PLA controllers can successfully navigate the robot to a target in the two-dimensional environment while avoiding collisions with randomly positioned obstacles.