"Learning from Scarce Experience"
        -------------______________________

  Leonid Peshkin {pesha at hms dot harvard period edu}
                   AND
  Christian R. Shelton {cshelton at cs.stanford.edu}


ICML 2002, Proceedings of the Nineteenth International Conference in Machine
           Learning. (Sydney, Australia) 

Abstract:

Searching the space of policies directly for the optimal 
policy has been one popular method for solving partially 
observable reinforcement learning problems. Typically, 
with each change of the target policy, its value is
estimated from the results of following that very policy. 
This requires a large number of interactions with the 
environment as different polices are considered. 

We present a family of algorithms based on likelihood ratio
estimation that use data gathered when executing one policy 
(or collection of policies) to estimate the value of a 
different policy. The algorithms combine estimation and 
optimization stages. The former utilizes experience to build
a non-parametric representation of an optimized function. 
The latter performs optimization on this estimate. We show 
positive empirical results and provide the sample complexity 
bound.