Variational Inference of Dirichlet Process Mixture using Stochastic Gradient Ascent

2020 ◽  
Author(s):  
Kart-Leong Lim
Keyword(s):  
Dirichlet Process ◽  
Variational Inference ◽  
Stochastic Gradient ◽  
Dirichlet Process Mixture ◽  
Gradient Ascent ◽  
Stochastic Gradient Ascent

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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