Drive-Reinforcement Learning System Applications
Abstract
The application of Drive-Reinforcement (D-R) to the unsupervised learning of manipulator 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 31, 1992
- Accession Number
- ADA264514
Entities
People
- Daniel W. Johnson
Organizations
- Martin Marietta