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Found 6 papers, 5 papers with code
iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvement
However, the existing RL-based TSC system often overlooks imperfect observations caused by degraded communication, such as packet loss, delays, and noise, as well as rare real-life events not included in the reward function, such as unconsidered emergency vehicles.
Traffic Signal Cycle Control with Centralized Critic and Decentralized Actors under Varying Intervention Frequencies
Traffic congestion in urban areas is a significant problem, leading to prolonged travel times, reduced efficiency, and increased environmental concerns.
LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environments
Specifically, a hybrid framework that augments LLMs with a suite of perception and decision-making tools is proposed, facilitating the interrogation of both the static and dynamic traffic information.
UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal Control
Traffic congestion is a persistent problem in urban areas, which calls for the development of effective traffic signal control (TSC) systems.
An Approximate Dynamic Programming Approach to Vehicle Platooning Coordination in Networks
To simplify the problem, we decouple the action space by prioritizing routing decisions based on travel time estimation.
ADLight: A Universal Approach of Traffic Signal Control with Augmented Data Using Reinforcement Learning
In this paper, we propose a novel reinforcement learning approach with augmented data (ADLight) to train a universal model for intersections with different structures.
