Reinforcement learning with iterative reasoning for merging in dense traffic
Maneuvering in dense traffic is a challenging task for autonomous vehicles because it
requires reasoning about the stochastic behaviors of many other participants. In addition, the …
requires reasoning about the stochastic behaviors of many other participants. In addition, the …
A multi-agent reinforcement learning method with curriculum transfer for large-scale dynamic traffic signal control
X Li, J Li, H Shi - Applied Intelligence, 2023 - Springer
Using reinforcement learning to control traffic signal systems has been discussed in recent
years, but most works focused on simple scenarios such as a single crossroads, and the …
years, but most works focused on simple scenarios such as a single crossroads, and the …
Auto-learning communication reinforcement learning for multi-intersection traffic light control
Multi-agent reinforcement learning is a promising solution to achieve intelligent traffic light
control by regarding each intersection as an independent agent. However, agents encounter …
control by regarding each intersection as an independent agent. However, agents encounter …
Traffic signal priority control based on shared experience multi‐agent deep reinforcement learning
Z Wang, K Yang, L Li, Y Lu… - IET Intelligent Transport …, 2023 - Wiley Online Library
Abstract Deep Reinforcement Learning (DRL) has demonstrated its great potential for
Adaptive Traffic Signal Control (ATSC) tasks at single‐intersection. In the transportation …
Adaptive Traffic Signal Control (ATSC) tasks at single‐intersection. In the transportation …
Cooperative Decision-Making for Mixed Traffic at an Unsignalized Intersection Based on Multi-Agent Reinforcement Learning
H Zhuang, C Lei, Y Chen, X Tan - Applied Sciences, 2023 - mdpi.com
Despite rapid advances in vehicle intelligence and connectivity, there is still a significant
period in mixed traffic where connected, automated vehicles and human-driven vehicles …
period in mixed traffic where connected, automated vehicles and human-driven vehicles …
TraCo: Learning Virtual Traffic Coordinator for Cooperation with Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) has emerged as a popular technique in diverse
domains due to its ability to automate system controller design and facilitate continuous …
domains due to its ability to automate system controller design and facilitate continuous …
Towards a very large scale traffic simulator for multi-agent reinforcement learning testbeds
Smart traffic control and management become an emerging application for Deep
Reinforcement Learning (DRL) to solve traffic congestion problems in urban networks …
Reinforcement Learning (DRL) to solve traffic congestion problems in urban networks …
Safe reinforcement learning with scene decomposition for navigating complex urban environments
Navigating urban environments represents a complex task for automated vehicles. They
must reach their goal safely and efficiently while considering a multitude of traffic …
must reach their goal safely and efficiently while considering a multitude of traffic …
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 …
DuaLight: Enhancing Traffic Signal Control by Leveraging Scenario-Specific and Scenario-Shared Knowledge
Reinforcement learning has been revolutionizing the traditional traffic signal control task,
showing promising power to relieve congestion and improve efficiency. However, the …
showing promising power to relieve congestion and improve efficiency. However, the …