A Reinforcement Learning Approach to On-line Optimal Control
An, P.E., Aslam-Mir, S., Brown, M. and Harris, C.J. (1994) A Reinforcement Learning Approach to On-line Optimal Control. Int. Conf. on Neural Networks , 2465-2471.
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Description/Abstract
This paper presents a hybrid control architecture for solving on-line optimal control. In this architecture, the control law is dynamically scheduled between a reinforcement controller and a stabilizing controller so that the closed-loop performance is smoothly transformed from a reactive behavior to one which can predict. Based on a modified Q-learning technique, the reinforcement controller is made of two components: policy and Q functions. The policy function is explicitly incorporated so as to bypass the minimum operator normally required for selecting actions and updating the Q function. This architecture is then applied to a repetitive operation using a second-order linear-time-variant plant with a nonlinear control structure. In this operation, the reinforcement signals are based on set-point errors and the reinforcement controller is generalized using second-order B-Splines networks. This example illustrates how, for a non-optimally tuned stabilizing controller, the closed-loop performance can be bootstrapped with the use of reinforcement learning. Results shows that the set-point performance of the hybrid controller is improved over that of the fixed structure controller by discovering better control strategies which compensate for the non-optimal gains and nonlinear control structure.
| Item Type: | Conference or Workshop Item (UNSPECIFIED) |
|---|---|
| Additional Information: | Organisation: IEEE Address: Orlando, Fl |
| Divisions: | Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control |
| Item ID: | 250209 |
| Date Deposited: | 04 May 1999 |
| Last Modified: | 02 Mar 2012 11:37 |
| Contributors: | An, P.E. (Author) Aslam-Mir, S. (Author) Brown, M. (Author) Harris, C.J. (Author) |
| Date: | 1994 |
| Additional Information: | Organisation: IEEE Address: Orlando, Fl |
| Status: | Published |
| Further Information: | Google Scholar |
| ISI Citation Count: | 0 |
| URI: | http://eprints.soton.ac.uk/id/eprint/250209 |
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