Traffic signal control with deep reinforcement learning

Lau, Joseph Yi Zhe (2025) Traffic signal control with deep reinforcement learning. Final Year Project, UTAR.

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Abstract

Traffic congestion, one of the problems that impact a large population of mankind, has incurred loss in terms of time, fuel, money and pollution. The solution lies in traffic signal control (TSC), optimized control is the way to mitigate traffic congestion. With numerous efforts and research in this area, the methods have evolved from transportation theory to deep reinforcement learning (DRL) approaches over the years. This report presents extensive research on various direction in this domain and identifies the gap of previous research and real-world deployment. The project restudies the nature of the problem, and therefore, propose a new formulation of Markov decision process (MDP) and framework in TSC to improve efficiency and generalizability of the algorithm in various scenario. Furthermore, this project explores the improvement of Soft Actor Critic (SAC) with gradient-based meta learning (GBML) method. Comprehensive experiments are conducted on Simulation of Urban Mobility (SUMO) to evaluate the effectiveness of the algorithm.

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