Traffic signal control is safety-critical for our daily life. Roughly one-quarter of road accidents
in the US happen at intersections due to problematic signal timing, urging the development …

Abstract Evaluations of Deep Reinforcement Learning (DRL) methods are an integral part of
scientific progress of the field. Beyond designing DRL methods for general intelligence …

Recent years have witnessed the rapid development of the Cooperative Vehicle
Infrastructure System (CVIS), where road infrastructures such as traffic lights (TL) and …

This paper introduces a library for cross-simulator comparison of reinforcement learning
models in traffic signal control tasks. This library is developed to implement recent state-of …

Traffic congestion is a persistent problem in urban areas, which calls for the development of
effective traffic signal control (TSC) systems. While existing Reinforcement Learning (RL) …

Reinforcement learning has been revolutionizing the traditional traffic signal control task,
showing promising power to relieve congestion and improve efficiency. However, the …

Reinforcement learning (RL) can automatically learn a better policy through a trial-and-error
paradigm and has been adopted to revolutionize and optimize traditional traffic signal …

Applying reinforcement learning (RL) to sparse reward domains is notoriously challenging
due to insufficient guiding signals. Common RL techniques for addressing such domains …

Recent research on reinforcement learning (RL) based traffic management shows promising
results, yet it is a significant issue due to increasing volume of traffic and lack of real time …

Cooperative control of vehicle trajectories and traffic signal phases is a promising approach
to improving the efficiency and safety of transportation systems. This type of traffic flow …