A survey on interpretable reinforcement learning
Although deep reinforcement learning has become a promising machine learning approach
for sequential decision-making problems, it is still not mature enough for high-stake domains …
for sequential decision-making problems, it is still not mature enough for high-stake domains …
Attendlight: Universal attention-based reinforcement learning model for traffic signal control
A Oroojlooy, M Nazari… - Advances in Neural …, 2020 - proceedings.neurips.cc
We propose AttendLight, an end-to-end Reinforcement Learning (RL) algorithm for the
problem of traffic signal control. Previous approaches for this problem have the shortcoming …
problem of traffic signal control. Previous approaches for this problem have the shortcoming …
[PDF][PDF] Alleviating Road Traffic Congestion with Artificial Intelligence.
G Sharon - IJCAI, 2021 - people.engr.tamu.edu
This paper reviews current AI solutions towards road traffic congestion alleviation. Three
specific AI technologies are discussed,(1) intersection management protocols for …
specific AI technologies are discussed,(1) intersection management protocols for …
Reinforcement learning benchmarks for traffic signal control
We propose a toolkit for developing and comparing reinforcement learning (RL)-based traffic
signal controllers. The toolkit includes implementation of state-of-the-art deep-RL algorithms …
signal controllers. The toolkit includes implementation of state-of-the-art deep-RL algorithms …
Safelight: A reinforcement learning method toward collision-free traffic signal control
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 …
in the US happen at intersections due to problematic signal timing, urging the development …
The impact of task underspecification in evaluating deep reinforcement learning
V Jayawardana, C Tang, S Li… - Advances in Neural …, 2022 - proceedings.neurips.cc
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 …
scientific progress of the field. Beyond designing DRL methods for general intelligence …
Extensible prototype learning for real‐time traffic signal control
Y Han, H Lee, Y Kim - Computer‐Aided Civil and Infrastructure …, 2023 - Wiley Online Library
Congestion resolution continues to remain a challenge even though various signal control
systems have been developed for traffic‐intersection control. To address this issue …
systems have been developed for traffic‐intersection control. To address this issue …
Multi-agent reinforcement learning for traffic signal control through universal communication method
How to coordinate the communication among intersections effectively in real complex traffic
scenarios with multi-intersection is challenging. Existing approaches only enable the …
scenarios with multi-intersection is challenging. Existing approaches only enable the …
Deep representation learning: Fundamentals, perspectives, applications, and open challenges
KT Baghaei, A Payandeh, P Fayyazsanavi… - arXiv preprint arXiv …, 2022 - arxiv.org
Machine Learning algorithms have had a profound impact on the field of computer science
over the past few decades. These algorithms performance is greatly influenced by the …
over the past few decades. These algorithms performance is greatly influenced by the …
Towards explainable traffic signal control for urban networks through genetic programming
The increasing number of vehicles in urban areas draws significant attention to traffic signal
control (TSC), which can enhance the efficiency of the entire network by properly switching …
control (TSC), which can enhance the efficiency of the entire network by properly switching …