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|Title:||PAC-Bayes Regularization for Learning Controllers that Generalize Across Environments|
|Abstract:||We bound the error rate of a robotic grasping controller in novel environments by connecting recent work involving PAC-Bayes regularization to algorithmic decision making. Training a controller on the KUKA robot in the Bullet physics simulator, we compute a PAC-Bayes on the learned controller and show generalization bounds on the performance of the controller in new environments.|
|Type of Material:||Princeton University Senior Theses|
|Appears in Collections:||Mathematics, 1934-2020|
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