Drive-Reinforcement Learning System Applications

摘要

The application of Drive-Reinforcement (D-R) to the unsupervised learning ofmanipulator control functions was investigated. In particular, the ability of a D-R neuronal system to learn servo-level and trajectory-level controls for a robotic mechanism was assessed. Results indicate that D-R based systems can be successful at learning these functions in real-time with actual hardware. Moreover, since the control architectures are generic, the evidence suggests that D-R would be effective in control system applications outside the robotics arena.... Drive-Reinforcement Learning, Neural Network Controllers, Robotics, Manipulator Kinematics, Dynamics and Control.