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Policy Search: Any Local Optimum Enjoys a Global Performance Guarantee

Bruno Scherrer 1 Matthieu Geist 2 
1 MAIA - Autonomous intelligent machine
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
2 MALIS - MAchine Learning and Interactive Systems
SUPELEC-Campus Metz, CentraleSupélec
Abstract : Local Policy Search is a popular reinforcement learning approach for handling large state spaces. Formally, it searches locally in a paramet erized policy space in order to maximize the associated value function averaged over some predefined distribution. It is probably commonly b elieved that the best one can hope in general from such an approach is to get a local optimum of this criterion. In this article, we show th e following surprising result: \emph{any} (approximate) \emph{local optimum} enjoys a \emph{global performance guarantee}. We compare this g uarantee with the one that is satisfied by Direct Policy Iteration, an approximate dynamic programming algorithm that does some form of Poli cy Search: if the approximation error of Local Policy Search may generally be bigger (because local search requires to consider a space of s tochastic policies), we argue that the concentrability coefficient that appears in the performance bound is much nicer. Finally, we discuss several practical and theoretical consequences of our analysis.
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Submitted on : Thursday, June 6, 2013 - 3:56:37 PM
Last modification on : Saturday, June 25, 2022 - 7:40:35 PM
Long-term archiving on: : Saturday, September 7, 2013 - 3:05:08 AM


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  • HAL Id : hal-00829548, version 1
  • ARXIV : 1306.1520


Bruno Scherrer, Matthieu Geist. Policy Search: Any Local Optimum Enjoys a Global Performance Guarantee. {date}. ⟨hal-00829548⟩



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