Deep reinforcement learning in transportation research: A review
Applying and adapting deep reinforcement learning (DRL) to tackle transportation problems
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …
Application of deep reinforcement learning in traffic signal control: An overview and impact of open traffic data
Persistent congestions which are varying in strength and duration in the dense traffic
networks are the most prominent obstacle towards sustainable mobility. Those types of …
networks are the most prominent obstacle towards sustainable mobility. Those types of …
Deep reinforcement learning for intelligent transportation systems: A survey
Latest technological improvements increased the quality of transportation. New data-driven
approaches bring out a new research direction for all control-based systems, eg, in …
approaches bring out a new research direction for all control-based systems, eg, in …
Toward a thousand lights: Decentralized deep reinforcement learning for large-scale traffic signal control
Traffic congestion plagues cities around the world. Recent years have witnessed an
unprecedented trend in applying reinforcement learning for traffic signal control. However …
unprecedented trend in applying reinforcement learning for traffic signal control. However …
Multi-agent deep reinforcement learning for urban traffic light control in vehicular networks
As urban traffic condition is diverse and complicated, applying reinforcement learning to
reduce traffic congestion becomes one of the hot and promising topics. Especially, how to …
reduce traffic congestion becomes one of the hot and promising topics. Especially, how to …
A deep reinforcement learning-based cooperative approach for multi-intersection traffic signal control
Abstract Recently, Adaptive Traffic Signal Control (ATSC) in the multi-intersection system is
considered as one of the most critical issues in Intelligent Transportation Systems (ITS) …
considered as one of the most critical issues in Intelligent Transportation Systems (ITS) …
[PDF][PDF] Heterogeneous Multi-Agent Deep Reinforcement Learning for Traffic Lights Control.
JA Calvo, I Dusparic - AICS, 2018 - aiai.ucd.ie
Reinforcement Learning (RL) has been extensively used in Urban Traffic Control (UTC)
optimization due its capability to learn the dynamics of complex problems from interactions …
optimization due its capability to learn the dynamics of complex problems from interactions …
Traffic signal control using end-to-end off-policy deep reinforcement learning
An efficient transportation system can substantially benefit our society, but road intersections
have always been among the major traffic bottlenecks leading to traffic congestion …
have always been among the major traffic bottlenecks leading to traffic congestion …
Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimization
Traffic congestion in metropolitan areas is a world-wide problem that can be ameliorated by
traffic lights that respond dynamically to real-time conditions. Recent studies applying deep …
traffic lights that respond dynamically to real-time conditions. Recent studies applying deep …
Deep Q learning-based traffic signal control algorithms: Model development and evaluation with field data
To contend traffic congestion on urban networks, existing studies have made great efforts to
develop traffic-responsive signal timing algorithms in the last decade. More recently, as an …
develop traffic-responsive signal timing algorithms in the last decade. More recently, as an …