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Within the field of robotics, much recent attention has been given to control techniques that have been termed reactive or behavior-based. The design of such control systems for even a remotely interesting task is typically a laborious effort, requiring many hours of experimental "tweaking" as the actual behavior of the system is observed by the system(More)
This paper describes work in progress on a neural-based reinforcement learning architecture for the design of reactive control policies for an autonomous robot. Reinforcement learning techniques allow a programmer to specify the control program at the level of the desired behavior of the robot, rather than at the level of the program that generates the(More)
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