Combining Multi-Objective Planning with Reinforcement Learning to Solve Complex Tasks in Environments with Sparse Rewards

Master thesis (2023)

Authors

C. van Rijn Electrical Engineering, Mathematics and Computer Science

Contributors

A. Lukina Algorithmics - EEMCS (supervisor 1)
M.T.J. Spaan Algorithmics - EEMCS (supervisor 2)
F.A. Oliehoek Interactive Intelligence - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Cas van Rijn
Sparse rewards Planning Reinforcement learning Sequential decision making LTL tasks
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Published Date

21-03-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

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