Corpus ID: 235457996

# Mungojerrie: Reinforcement Learning of Linear-Time Objectives

@article{Hahn2021MungojerrieRL,
title={Mungojerrie: Reinforcement Learning of Linear-Time Objectives},
author={E. M. Hahn and Mateo Perez and S. Schewe and F. Somenzi and A. Trivedi and D. Wojtczak},
journal={ArXiv},
year={2021},
volume={abs/2106.09161}
}
Reinforcement learning synthesizes controllers without prior knowledge of the system. At each timestep, a reward is given. The controllers optimize the discounted sum of these rewards. Applying this class of algorithms requires designing a reward scheme, which is typically done manually. The designer must ensure that their intent is accurately captured. This may not be trivial, and is prone to error. An alternative to this manual programming, akin to programming directly in assembly, is to… Expand

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