scholarly journals Reinforcement Learning for Building Environmental Control

10.5772/5286 ◽  
2008 ◽  
Author(s):  
Konstantinos Dalamagkidis ◽  
Dionysia Kolokots
Keyword(s):  
Reinforcement Learning ◽  
Environmental Control

Thermal Considerations in Lens Regulator Systems

1970 ◽  
Vol 28 ◽  
pp. 358-359
Author(s):  
K.C. Newton
Keyword(s):  
Electron Microscope ◽  
Thermal Effects ◽  
Environmental Control ◽  
Reference Networks ◽  
Better Than

Thermal effects in lens regulator systems have become a major problem with the extension of electron microscope resolution capabilities below 5 Angstrom units. Larger columns with immersion lenses and increased accelerating potentials have made solutions more difficult by increasing the power being handled. Environmental control, component choice, and wiring design provide answers, however. Figure 1 indicates with broken lines where thermal problems develop in regulator systemsExtensive environmental control is required in the sampling and reference networks. In each case, stability better than I ppm/min. is required. Components with thermal coefficients satisfactory for these applications without environmental control are either not available or priced prohibitively.

Bayes factors for reinforcement-learning models of the Iowa gambling task.

Decision ◽  
2016 ◽  
Vol 3 (2) ◽  
pp. 115-131 ◽  
Author(s):  
Helen Steingroever ◽  
Ruud Wetzels ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Reinforcement Learning ◽  
Iowa Gambling Task ◽  
Bayes Factors ◽  
Gambling Task ◽  
Learning Models ◽  
Reinforcement Learning Models
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