most citedLearning Traffic Signal Control via Genetic Programming

10 citations · 14 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2025

SymLight: Exploring Interpretable and Deployable Symbolic Policies for Traffic Signal Control

Xiao-Cheng Liao, Yi Mei, Mengjie Zhang

Deep Reinforcement Learning have achieved significant success in automatically devising effective traffic signal control (TSC) policies. Neural policies, however, tend to be over-p…

cs.LG2025★ 4 cited

GPLight+: A Genetic Programming Method for Learning Symmetric Traffic Signal Control Policy

Xiao-Cheng Liao, Yi Mei, Mengjie Zhang

Recently, learning-based approaches, have achieved significant success in automatically devising effective traffic signal control strategies. In particular, as a powerful evolution…

cs.LG2025

Generalized Phase Pressure Control Enhanced Reinforcement Learning for Traffic Signal Control

Xiao-Cheng Liao, Yi Mei, Mengjie Zhang +1

Appropriate traffic state representation is crucial for learning traffic signal control policies. However, most of the current traffic state representations are heuristically desig…

cs.MA2024

Emergent Crowd Grouping via Heuristic Self-Organization

Xiao-Cheng Liao, Wei-Neng Chen, Xiang-Ling Chen +1

Modeling crowds has many important applications in games and computer animation. Inspired by the emergent following effect in real-life crowd scenarios, in this work, we develop a…

cs.AI2024★ 10 cited

Learning Traffic Signal Control via Genetic Programming

Xiao-Cheng Liao, Yi Mei, Mengjie Zhang

The control of traffic signals is crucial for improving transportation efficiency. Recently, learning-based methods, especially Deep Reinforcement Learning (DRL), garnered substant…