Solving Decentralized Multi-Agent Pickup and Delivery Problems using Reinforcement Learning in a Simulated Environment
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2021-07-10
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en
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Abstract
Due to technological advancements the possibilities for robotic automa-
tion are growing. There is increasing demand and necessity for robotic
systems which intelligently and e ciently transport goods. Systems exist
which can carry out such tasks, but they lack general employability, only
being suitable for controlled environments at limited scale. Decentralized
systems could solve these issues and allow robotic logistical systems to be
more versatile and work on a larger scale. A simulation was created to test
if reinforcement learning could be used to train agents in order to create
such a decentralized system. It was found that in an abstract simulated
environment this was possible. Furthermore, the results suggested that if
robots in such a system can communicate information about their status to
one another, the system's performance can be increased.
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Faculteit der Sociale Wetenschappen