Efficient federated learning with spike neural networks for traffic sign recognition
With the gradual popularization of self-driving, it is becoming increasingly important for
vehicles to smartly make the right driving decisions and autonomously obey traffic rules by …
vehicles to smartly make the right driving decisions and autonomously obey traffic rules by …
Multi-agent broad reinforcement learning for intelligent traffic light control
Intelligent traffic light control (ITLC) aims to relieve traffic congestion. Some multi-agent deep
reinforcement learning (MADRL) algorithms have been proposed for ITLC, and most of them …
reinforcement learning (MADRL) algorithms have been proposed for ITLC, and most of them …
An introduction to multi-agent reinforcement learning and review of its application to autonomous mobility
Many scenarios in mobility and traffic involve multiple different agents that need to cooperate
to find a joint solution. Recent advances in behavioral planning use Reinforcement Learning …
to find a joint solution. Recent advances in behavioral planning use Reinforcement Learning …
A survey on modeling for behaviors of complex intelligent systems based on generative adversarial networks
Y Lv, J Duan, X Li - Computer Science Review, 2024 - Elsevier
This paper provides an extensive and in-depth survey of behavior modeling for complex
intelligent systems, focusing specifically on the innovative applications of Generative …
intelligent systems, focusing specifically on the innovative applications of Generative …
大规模智慧交通信号控制中的强化学习和深度强化学习方法综述.
翟子洋， 郝茹茹， 董世浩 - Application Research of …, 2024 - search.ebscohost.com
当前在交通信号控制系统中引入智能化检测和控制已是大势所趋, 特别是强化学习和深度强化
学习方法在可扩展性, 稳定性和可推广性等方面展现出巨大的技术优势, 已成为该领域的研究 …
学习方法在可扩展性, 稳定性和可推广性等方面展现出巨大的技术优势, 已成为该领域的研究 …
Integrating sustainability in future traffic lighting: Designing efficient light systems for vehicle, road, and traffic
Lighting systems in intelligent transportation system (ITS) can play a crucial role in
promoting sustainability through various measures, including enhanced driving safety …
promoting sustainability through various measures, including enhanced driving safety …
Deep Generative Adversarial Reinforcement Learning for Semi-Supervised Segmentation of Low-Contrast and Small Objects in Medical Images
Deep reinforcement learning (DRL) has demonstrated impressive performance in medical
image segmentation, particularly for low-contrast and small medical objects. However …
image segmentation, particularly for low-contrast and small medical objects. However …
Adaptive multi-agent deep mixed reinforcement learning for traffic light control
Despite significant advancements in Multi-Agent Deep Reinforcement Learning (MADRL)
approaches for Traffic Light Control (TLC), effectively coordinating agents in diverse traffic …
approaches for Traffic Light Control (TLC), effectively coordinating agents in diverse traffic …
eMARLIN: distributed coordinated adaptive traffic signal control with topology-embedding propagation
In this paper, we examine the practical problem of minimizing the delay in traffic networks
that are controlled at each intersection independently, without a centralized supervisory …
that are controlled at each intersection independently, without a centralized supervisory …
[PDF][PDF] Model-based Sparse Communication in Multi-agent Reinforcement Learning
Learning to communicate efficiently is central to multi-agent reinforcement learning (MARL).
Existing methods often require agents to exchange messages intensively, which abuses …
Existing methods often require agents to exchange messages intensively, which abuses …