Reward Definitions in Reinforcement Learning for Traffic Light Control

Bachelor thesis (2020)

Authors

C.P. Jansen Electrical Engineering, Mathematics and Computer Science

Contributors

M. Suau Interactive Intelligence - EEMCS (mentor)
F.A. Oliehoek Interactive Intelligence - EEMCS (graduation committee member)
M.A. Migut Computer Science & Engineering-Teaching Team - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2020 Cian Jansen
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Published Date

22-06-2020

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Traffic congestion is a problem of tremendous size that affects many people. Using Reinforcement Learning to find a light control policy can ease traffic congestion and decrease travel time for vehicles. This paper specifically looks at the effect of using different reward functions for training agents. We highlight how the learnabilty of a reward function and its alignment with the final goal of the agent are the most important factors when designing a reward definition. Finally we propose a reward function to use for future