Print Email Facebook Twitter Learning and Optimizing Probabilistic Models for Planning under Uncertainty Title Learning and Optimizing Probabilistic Models for Planning under Uncertainty Author van Bekkum, Rob (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Spaan, M.T.J. (mentor) Loog, M. (graduation committee) Kober, J. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science Date 2017-09-27 Abstract Decision-theoretic planning techniques are increasingly being used to obtain (optimal) plans for domains involving uncertainty, which may be present in the form of the controlling agent's actions, its percepts, or exogenous factors in the domain. These techniques build on detailed probabilistic models of the underlying system, for which Markov Decision Processes (MDPs) have become the de facto standard formalism. However, handcrafting these probabilistic models is usually a daunting and error-prone task, requiring expert knowledge on the domain under consideration. Therefore, it is desirable to automate the process of obtaining these models by means of learning algorithms presented with a set of execution traces from the system. Although some work has already been done on crafting such learning algorithms, the state of the art lacks an automated method of configuring their hyperparameters, so to maximize the performance yielded from executing the derived plans. In this work we present a method that employs the Bayesian Optimization (BO) framework to learn MDPs autonomously from a set of execution traces, optimizing the expected value and performance in simulations over a set of tasks the underlying system is expected to perform. The approach has been tested on learning MDPs for mobile robot navigation, motivated by the significant uncertainty accompanying the robots' actions in this domain. Subject planning under uncertaintyBayesian Optimizationprobabilistic model learningMarkov Decision Processesdecision-theoretic planningAlgorithmics To reference this document use: http://resolver.tudelft.nl/uuid:37e80be9-ab78-427b-b317-c5529a752d7d Part of collection Student theses Document type master thesis Rights © 2017 Rob van Bekkum Files PDF Learning_and_Optimizing_P ... tainty.pdf 4.7 MB Close viewer /islandora/object/uuid:37e80be9-ab78-427b-b317-c5529a752d7d/datastream/OBJ/view