Learning to Communicate with Reinforcement Learning for an Adaptive
Traffic Control System
- URL: http://arxiv.org/abs/2110.15779v1
- Date: Fri, 29 Oct 2021 13:46:15 GMT
- Title: Learning to Communicate with Reinforcement Learning for an Adaptive
Traffic Control System
- Authors: Simon Vanneste, Gauthier de Borrekens, Stig Bosmans, Astrid Vanneste,
Kevin Mets, Siegfried Mercelis, Steven Latr\'e, Peter Hellinckx
- Abstract summary: We investigate independent Q-learning (IQL) without communication and differentiable inter-agent learning (DIAL) with learned communication on an adaptive traffic control system (ATCS)
Our results show that a DIAL agent outperforms an independent Q-learner on both training time and on maximum achieved reward as it is able to share relevant information with the other agents.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent work in multi-agent reinforcement learning has investigated inter
agent communication which is learned simultaneously with the action policy in
order to improve the team reward. In this paper, we investigate independent
Q-learning (IQL) without communication and differentiable inter-agent learning
(DIAL) with learned communication on an adaptive traffic control system (ATCS).
In real world ATCS, it is impossible to present the full state of the
environment to every agent so in our simulation, the individual agents will
only have a limited observation of the full state of the environment. The ATCS
will be simulated using the Simulation of Urban MObility (SUMO) traffic
simulator in which two connected intersections are simulated. Every
intersection is controlled by an agent which has the ability to change the
direction of the traffic flow. Our results show that a DIAL agent outperforms
an independent Q-learner on both training time and on maximum achieved reward
as it is able to share relevant information with the other agents.
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