A continuous reinforcement learning strategy for closed-loop control in fluid dynamics

Author(s):  
Charles Pivot ◽  
Laurent Cordier ◽  
Lionel Mathelin
2016 ◽  
Vol 30 (6) ◽  
pp. 497-510 ◽  
Author(s):  
Florimond Guéniat ◽  
Lionel Mathelin ◽  
M. Yousuff Hussaini

2021 ◽  
Vol 24 (2) ◽  
pp. 1807-1813
Author(s):  
Brett L. Moore Brett L. Moore ◽  
Periklis Panousis ◽  
Vivek Kulkarni ◽  
Larry Pyeatt ◽  
Anthony G. Doufas Anthony G. Doufas

Research has demonstrated the efficacy of closed-loop control of anesthesia using the bispectral index (BIS) of the electroencephalogram as the controlled variable, and the development of model-based, patient-adaptive systems has considerably improved anesthetic control. To further explore the use of model-based control in anesthesia, we investigated the application of reinforcement learning (RL) in the delivery of patient-specific, propofol-induced hypnosis in human volunteers. When compared to published performance metrics, RL control demonstrated accuracy and stability, indicating that further, more rigorous clinical study is warranted.


2012 ◽  
Vol 220 (1) ◽  
pp. 3-9 ◽  
Author(s):  
Sandra Sülzenbrück

For the effective use of modern tools, the inherent visuo-motor transformation needs to be mastered. The successful adjustment to and learning of these transformations crucially depends on practice conditions, particularly on the type of visual feedback during practice. Here, a review about empirical research exploring the influence of continuous and terminal visual feedback during practice on the mastery of visuo-motor transformations is provided. Two studies investigating the impact of the type of visual feedback on either direction-dependent visuo-motor gains or the complex visuo-motor transformation of a virtual two-sided lever are presented in more detail. The findings of these studies indicate that the continuous availability of visual feedback supports performance when closed-loop control is possible, but impairs performance when visual input is no longer available. Different approaches to explain these performance differences due to the type of visual feedback during practice are considered. For example, these differences could reflect a process of re-optimization of motor planning in a novel environment or represent effects of the specificity of practice. Furthermore, differences in the allocation of attention during movements with terminal and continuous visual feedback could account for the observed differences.


Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 118-LB
Author(s):  
CAROL J. LEVY ◽  
GRENYE OMALLEY ◽  
SUE A. BROWN ◽  
DAN RAGHINARU ◽  
YOGISH C. KUDVA ◽  
...  

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 101-LB
Author(s):  
SUE A. BROWN ◽  
DAN RAGHINARU ◽  
BRUCE A. BUCKINGHAM ◽  
YOGISH C. KUDVA ◽  
LORI M. LAFFEL ◽  
...  

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