Vassilis Papavassiliou and Stuart Russell

EECS Department
University of California, Berkeley
Technical Report No. UCB/CSD-98-1005
May 1998

A new algorithm is described for value determination in Markov decision processes. The algorithm works with arbitrary approximate representations of the value function. We show that if the approximating family is agnostically PAC-learnable, then the algorithm converges to a solution that is close to the globally optimal solution in the approximating family.

BibTeX citation:

@techreport{Papavassiliou:CSD-98-1005,
    Author = {Papavassiliou, Vassilis and Russell, Stuart},
    Title = {Value Determination with General Function Approximators},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {1998},
    Month = {May},
    URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/1998/6411.html},
    Number = {UCB/CSD-98-1005},
    Abstract = {A new algorithm is described for value determination in Markov decision processes. The algorithm works with arbitrary approximate representations of the value function. We show that if the approximating family is agnostically PAC-learnable, then the algorithm converges to a solution that is close to the globally optimal solution in the approximating family.}
}

EndNote citation:

%0 Report
%A Papavassiliou, Vassilis
%A Russell, Stuart
%T Value Determination with General Function Approximators
%I EECS Department, University of California, Berkeley
%D 1998
%@ UCB/CSD-98-1005
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/1998/6411.html
%F Papavassiliou:CSD-98-1005