Traffic Light Control by Multiagent Reinforcement Learning Systems

  title={Traffic Light Control by Multiagent Reinforcement Learning Systems},
  author={B. Bakker and S. Whiteson and L. Kester and F. Groen},
  booktitle={Interactive Collaborative Information Systems},
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.Expand
Intelligent traffic light control using collaborative Q-Learning algorithms
In most large cities with high population densities, there are many problems that arise due to traffic jam as it happens in metropolitan cities in Indonesia. There are many factors that contribute toExpand
Multi-agent reinforcement learning for traffic signal control
This paper forms the TSC problem as a discounted cost Markov decision process (MDP) and applies multi-agent reinforcement learning (MARL) algorithms to obtain dynamic TSC policies and shows that these algorithms perform significantly better than both the standard fixed signal timing (FST) algorithm and the saturation balancing (SAT) algorithm over two real road networks. Expand
Deep Reinforcement Learning based Traffic Signal optimization for Multiple Intersections in ITS
  • Ananya Paul, S. Mitra
  • Computer Science
  • 2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)
  • 2020
A single DRL agent manages the traffic signal of multiple intersections using policy gradient algorithm that is trained with spatio-temporal data of the environment that allows it to perform action in different deep neural network models. Expand
Multi-Agent Reinforcement Learning for Integrated Network of Adaptive Traffic Signal Controllers (MARLIN-ATSC)
  • Samah El-Tantawy, B. Abdulhai
  • Engineering, Computer Science
  • 2012 15th International IEEE Conference on Intelligent Transportation Systems
  • 2012
A novel, decentralized and coordinated adaptive real-time traffic signal control system using Multi-Agent Reinforcement Learning for Integrated Network of Adaptive Traffic Signal Controllers (MARLINATSC) that aims to minimize the total vehicle delay in the traffic network. Expand
IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control
This paper proposes a more effective deep reinforcement learning model for traffic light control and tests the method on a large-scale real traffic dataset obtained from surveillance cameras. Expand
An Experimental Review of Reinforcement Learning Algorithms for Adaptive Traffic Signal Control
This chapter presents a comprehensive review of the applications of RL to the traffic control problem to date, along with a case study that showcases the developing multi-agent traffic control architecture. Expand
Deep reinforcement learning for urban traffic light control
Traffic light timing optimization is still an active line of research despite the wealth of scientific literature on the topic, and the problem remains unsolved for any non-toy scenario. One of theExpand
Optimised Traffic Light Management Through Reinforcement Learning: Traffic State Agnostic Agent vs. Holistic Agent With Current V2I Traffic State Knowledge
This study designs representative Deep Reinforcement Learning agents that learn the control of multiple traffic lights without and with current traffic state information, and finds that the holistic system substantially increases average vehicle velocities and flow rates, while reducing CO2 emissions, average wait and trip times, as well as a driver stress metric. Expand
Multi-intersections traffic signal intelligent control using collaborative q-learning algorithm
An urban road traffic area-wide coordination control algorithm based on collaborative Q-learning is proposed and the agent model of traffic intersections is demonstrated, showing that the control algorithm can effectively reduce the average delay time and play a very good control effect with multi-intersections. Expand
Deep Deterministic Policy Gradient for Urban Traffic Light Control
This work proposes to rely on the ability of deep Learning approaches to handle large input spaces, in the form of Deep Deterministic Policy Gradient (DDPG) algorithm, and performs several experiments with a range of models. Expand


Reinforcement Learning of Traffic Light Controllers Adapting to Traffic Congestion
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. Expand
Simulation and optimization of traffic in a city
Optimal traffic light control is a multi-agent decision problem, for which we propose to use reinforcement learning algorithms. Our algorithm learns the expected waiting times of cars for red andExpand
Reinforcement learning for true adaptive traffic signal control
The ability to exert real-time, adaptive control of transportation processes is the core of many intelligent transportation systems decision support tools. Reinforcement learning, an artificialExpand
Intelligent Traffic Light Control
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. Expand
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. Expand
Adaptive traffic signal control using fuzzy logic
  • S. Chiu
  • Engineering
  • 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 signalExpand
Traffic-responsive signal timing for system-wide traffic control
  • J. Spall, D. C. Chin
  • Physics
  • Proceedings of the 1997 American Control Conference (Cat. No.97CH36041)
  • 1997
Due to the many complex aspects of a traffic system, it has been difficult to determine the optimal signal timing. Much of this difficulty has stemmed from the need to build extremely complex modelsExpand
Learning to Act Using Real-Time Dynamic Programming
An algorithm based on dynamic programming, which is called Real-Time DP, is introduced, by which an embedded system can improve its performance with experience and illuminate aspects of other DP-based reinforcement learning methods such as Watkins'' Q-Learning algorithm. Expand
Agent-based learning control method for urban traffic signal of single intersection
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. Expand
The implementation of a genetic algorithm (GA) (an artificial intelligence technique) to produce optimal or near-optimal intersection traffic signal timing strategies is described. The focus is onExpand