A Dual-Mode Multi-Agent Reinforcement Learning Method for Context-Aware Traffic Control Optimization

This publication has 21 references indexed in Scilit:

• Enhancing Urban Traffic Control with Priority-Aware Multi-Objective Reinforcement Learning
  2025
• Federated deep reinforcement learning-based urban traffic signal optimal control
  Scientific Reports, 2025
• Regional Multi-Agent Cooperative Reinforcement Learning for City-Level Traffic Grid Signal Control
  IEEE/CAA Journal of Automatica Sinica, 2024
• Federated Reinforcement Learning for Adaptive Traffic Signal Control: A Case Study in New York City
  2023
• Flexible Traffic Signal Control via Multi-Objective Reinforcement Learning
  IEEE Access, 2023
• Multi-Objective Traffic Signal Control Using Network-Wide Agent Coordinated Reinforcement Learning
  Expert Systems with Applications, 2023
• Dynamic traffic signal control using mean field multi‐agent reinforcement learning in large scale road‐networks
  IET Intelligent Transport Systems, 2023
• EMVLight: A multi-agent reinforcement learning framework for an emergency vehicle decentralized routing and traffic signal control system
  Transportation Research Part C: Emerging Technologies, 2022
• traffic flow prediction model based on deep belief network and genetic algorithm
  IET Intelligent Transport Systems, 2018
• A Decentralized Traffic Light Control System Based on Adaptive Learning
  IFAC-PapersOnLine, 2017