Variational posterior approximation using stochastic gradient ascent with adaptive stepsize

2021 ◽  
Vol 112 ◽  
pp. 107783
Author(s):  
Kart-Leong Lim ◽  
Xudong Jiang
Keyword(s):  
Stochastic Gradient ◽  
Gradient Ascent ◽  
Adaptive Stepsize ◽  
Stochastic Gradient Ascent ◽  
Posterior Approximation

Using Expectation-Maximization for Reinforcement Learning

1997 ◽  
Vol 9 (2) ◽  
pp. 271-278 ◽  
Author(s):  
Peter Dayan ◽  
Geoffrey E. Hinton
Keyword(s):  
Reinforcement Learning ◽  
Expectation Maximization ◽  
Learning Algorithm ◽  
Stochastic Gradient ◽  
Gradient Ascent ◽  
Relative Payoff ◽  
The Mean ◽  
Stochastic Gradient Ascent ◽  
Reinforcement Learning Algorithm

We discuss Hinton's (1989) relative payoff procedure (RPP), a static reinforcement learning algorithm whose foundation is not stochastic gradient ascent. We show circumstances under which applying the RPP is guaranteed to increase the mean return, even though it can make large changes in the values of the parameters. The proof is based on a mapping between the RPP and a form of the expectation-maximization procedure of Dempster, Laird, and Rubin (1977).

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