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Published in:
Logic Aggregators and Their Implementations Read chapter Read first chapter

2023 | OriginalPaper | Chapter

A Goal-Oriented Specification Language for Reinforcement Learning

Authors : Simon Schwan, Verena Klös, Sabine Glesner

Published in: Modeling Decisions for Artificial Intelligence

Publisher: Springer Nature Switzerland

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Abstract

The design of reinforcement learning (RL) agents is difficult, especially in domains with complex and possibly conflicting objectives such as autonomous driving. In addition to the formal nature of RL with high technical barriers, the fragility of the reward signal results in the common trial-and-error practice in the design of RL agents. We propose a novel goal-oriented specification language that is tailored to reinforcement learning but abstracts from technical details. To overcome the problematic trial-and-error practice, our specification language provides the foundation for an easy and systematic design process in RL.

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previous chapter Bayesian Logistic Model for Positive and Unlabeled Data
next chapter Improved Spectral Norm Regularization for Neural Networks
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Metadata
Title
A Goal-Oriented Specification Language for Reinforcement Learning
Authors
Simon Schwan
Verena Klös
Sabine Glesner
Copyright Year
2023
Book
Modeling Decisions for Artificial Intelligence

Print ISBN: 978-3-031-33497-9

Electronic ISBN: 978-3-031-33498-6

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-33498-6_12

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