Print Email Facebook Twitter Combining Multi-Objective Planning with Reinforcement Learning to Solve Complex Tasks in Environments with Sparse Rewards Title Combining Multi-Objective Planning with Reinforcement Learning to Solve Complex Tasks in Environments with Sparse Rewards Author van Rijn, Cas (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Lukina, A. (mentor) Spaan, M.T.J. (graduation committee) Oliehoek, F.A. (graduation committee) Degree granting institution Delft University of Technology Programme Electrical Engineering | Embedded Systems Date 2023-03-21 Abstract Sequential decision-making problems are problems where the goal is to find a sequence of actions that complete a task in an environment. A particularly difficult type of sequential decision-making problem to solve is one in which the environment has sparse rewards, a large state space, and where the goal is to complete a complex task. In this research we create a controller that can be used to solve these types of environments in cases where the task needs to be optimized for multiple objectives. We create MOPRL, an approach that combines techniques from planning, formal methods, and reinforcement learning to synthesize such a controller. W Subject Sequential decision makingReinforcement learningPlanningLTL tasksSparse rewards To reference this document use: http://resolver.tudelft.nl/uuid:1b0a4da5-d239-433b-b428-c927049e9055 Part of collection Student theses Document type master thesis Rights © 2023 Cas van Rijn Files PDF Thesis_Final_Cas_van_Rijn.pdf 2.01 MB Close viewer /islandora/object/uuid:1b0a4da5-d239-433b-b428-c927049e9055/datastream/OBJ/view