Using a Deep Reinforcement Learning Agent for Traffic Signal Control

2016-11-03 · Wade Genders, Saiedeh Razavi

Ensuring transportation systems are efficient is a priority for modern society. Technological advances have made it possible for transportation systems to collect large volumes of varied data on an unprecedented scale. We propose a traffic signal control system which takes advantage of this new, high quality data, with minimal abstraction compared to other proposed systems. We apply modern deep reinforcement learning methods to build a truly adaptive traffic signal control agent in the traffic microsimulator SUMO. We propose a new state space, the discrete traffic state encoding, which is information dense. The discrete traffic state encoding is used as input to a deep convolutional neural network, trained using Q-learning with experience replay. Our agent was compared against a one hidden layer neural network traffic signal control agent and reduces average cumulative delay by 82%, average queue length by 66% and average travel time by 20%.

📄 PDF Abstract BibTeX arXiv:1611.01142

Code (0)

등록된 구현이 없습니다.

Tasks

Deep Reinforcement LearningQ-Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Traffic Signal Control

Methods 이 논문이 사용한 방법론

Q-Learning Q-Learning is an off-policy temporal difference control algorithm: $$Q\left(S\_{t}, A\_{t}\right) \leftarrow Q\left(S\_{t}, A\_{t}\right) + \alpha\left[R_{t+1} +…

Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning

2017-04-28 · Seyed Sajad Mousavi, Michael Schukat, Enda Howley

Recent advances in combining deep neural network architectures with reinforcement learning techniques have shown promising potential results in solving complex control problems with high dimensional state and action spac…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

A Novel Multi-Agent Deep RL Approach for Traffic Signal Control

2023-06-05 · Shijie Wang, Shangbo Wang

As travel demand increases and urban traffic condition becomes more complicated, applying multi-agent deep reinforcement learning (MARL) to traffic signal control becomes one of the hot topics. The rise of Reinforcement …

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

Joint Optimization of Traffic Signal Control and Vehicle Routing in Signalized Road Networks using Multi-Agent Deep Reinforcement Learning

2023-10-16 · Xianyue Peng, Hang Gao, Gengyue Han, Hao Wang 외

Urban traffic congestion is a critical predicament that plagues modern road networks. To alleviate this issue and enhance traffic efficiency, traffic signal control and vehicle routing have proven to be effective measure…

Deep Reinforcement Learning Traffic Signal Control

Combat Urban Congestion via Collaboration: Heterogeneous GNN-based MARL for Coordinated Platooning and Traffic Signal Control

2023-10-17 · Xianyue Peng, Hang Gao, Shenyang Chen, Hao Wang 외

Over the years, reinforcement learning has emerged as a popular approach to develop signal control and vehicle platooning strategies either independently or in a hierarchical way. However, jointly controlling both in rea…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Traffic Signal Control

Adaptive Transit Signal Priority based on Deep Reinforcement Learning and Connected Vehicles in a Traffic Microsimulation Environment

2024-07-31 · Dickness Kwesiga, Angshuman Guin, Michael Hunter

Model free reinforcement learning (RL) provides a potential alternative to earlier formulations of adaptive transit signal priority (TSP) algorithms based on mathematical programming that require complex and nonlinear ob…

Deep Reinforcement Learning Reinforcement Learning (RL)