A reinforcement learning approach to on-line optimal control

2002 ◽  
Author(s):  
P.E. An ◽  
S. Aslam-Mir ◽  
M. Brown ◽  
C.J. Harris
Keyword(s):  
Optimal Control ◽  
Reinforcement Learning ◽  
Learning Approach ◽  
On Line

Kalman Filter Control Embedded into the Reinforcement Learning Framework

2004 ◽  
Vol 16 (3) ◽  
pp. 491-499 ◽  
Author(s):  
István Szita ◽  
András Lőrincz
Keyword(s):  
Optimal Control ◽  
Kalman Filter ◽  
Reinforcement Learning ◽  
Learning Rule ◽  
Slight Modification ◽  
Linear Quadratic ◽  
Filter Model ◽  
Learning Framework ◽  
Value Estimation ◽  
On Line

There is a growing interest in using Kalman filter models in brain modeling. The question arises whether Kalman filter models can be used on-line not only for estimation but for control. The usual method of optimal control of Kalman filter makes use of off-line backward recursion, which is not satisfactory for this purpose. Here, it is shown that a slight modification of the linear-quadratic-gaussian Kalman filter model allows the on-line estimation of optimal control by using reinforcement learning and overcomes this difficulty. Moreover, the emerging learning rule for value estimation exhibits a Hebbian form, which is weighted by the error of the value estimation.

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