Traffic Light Control by Multiagent Reinforcement Learning Systems

@inproceedings{Bakker2010TrafficLC,
  title={Traffic Light Control by Multiagent Reinforcement Learning Systems},
  author={Bram Bakker and Shimon Whiteson and Leon Kester and Frans C. A. Groen},
  booktitle={Interactive Collaborative Information Systems},
  year={2010}
}
Traffic light control is one of the main means of controlling road traffic. [] Key Method First, the general multi-agent reinforcement learning framework is described, which is used to control traffic lights in this work.

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References

SHOWING 1-10 OF 43 REFERENCES

Reinforcement Learning of Traffic Light Controllers Adapting to Traffic Congestion

TLDR
This paper describes the optimization of traffic light controllers using a multi-agent, model-based reinforcement learning or approximate real-time dynamic programming approach, and shows that this approach outperforms existing methods.

Simulation and optimization of traffic in a city

TLDR
The experimental results show that the adaptive algorithms can strongly reduce average waiting times of cars compared to three hand-designed controllers.

Reinforcement learning for true adaptive traffic signal control

TLDR
An introduction to Q-learning, a simple yet powerful reinforcement learning algorithm, is presented and a case study involving application to traffic signal control is presented, which involves optimal control of heavily congested traffic across a two-dimensional road network.

Intelligent Traffic Light Control

TLDR
This paper studies the simulation and optimization of traffic light controllers in a city and presents an adaptive optimization algorithm based on reinforcement learning that outperform other fixed controllers on all studied infrastructures.

INTELLIGENT TRAFFIC LIGHTS CONTROL BY FUZZY LOGIC

TLDR
The implementation of an intelligent traffic lights control system using fuzzy logic technology which has the capability of mimicking human intelligence for controlling traffic lights is discussed.

Adaptive traffic signal control using fuzzy logic

  • S. Chiu
  • Computer Science
    Proceedings of the Intelligent Vehicles `92 Symposium
  • 1992
Presents a distributed approach to traffic signal control, where the signal timing parameters are a given intersection are adjusted as functions of the local traffic condition and of the signal

Traffic-responsive signal timing for system-wide traffic control

  • J. SpallD. C. Chin
  • Computer Science
    Proceedings of the 1997 American Control Conference (Cat. No.97CH36041)
  • 1997
TLDR
The approach is based on a neural network serving as the basis for the control law, with the weight estimation occurring in closed-loop mode via the simultaneous perturbation stochastic approximation (SPSA) algorithm.

Learning to Act Using Real-Time Dynamic Programming

Agent-based learning control method for urban traffic signal of single intersection

TLDR
This technique that is combined with the rules and Q_learning is applied to urban traffic area to control dynamically the traffic signals in an isolated intersection and the result indicates that the effect of the new method is better.

SIGNAL TIMING DETERMINATION USING GENETIC ALGORITHMS

TLDR
The implementation of a genetic algorithm (GA) to produce optimal or near-optimal intersection traffic signal timing strategies is described, with a focus on examining this application within a simple traffic situation, giving the reader a clear understanding of how the genetic algorithm is used.