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Adapting control policies for expensive systems to changing environments
2011
2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
Many controlled systems must operate over a range of external conditions. In this paper, we focus on the problem of learning a policy to adapt a system's controller based on the value of these external conditions in order to always perform well (i.e., maximize system output). In addition, we are concerned with systems for which it is expensive to run experiments, and therefore restrict the number that can be run during training. We formally define the problem setup and the notion of an optimal
doi:10.1109/iros.2011.6095039
dblp:conf/iros/TeschSC11
fatcat:3am733hhbvddpfjh4hqt2cb2lm