##基于深度强化学习的智能交通信号控制

Abstract

This paper investigates the application of Deep Reinforcement Learning (DRL) for intelligent traffic signal control. Traditional traffic signal control methods often rely on pre-defined rules or simple optimization algorithms, which may not effectively adapt to dynamic traffic conditions. DRL offers a promising approach by allowing an agent to learn optimal control policies through trial and error interactions with the traffic environment. This research proposes a DRL framework for dynamic traffic signal control, aiming to mitigate congestion and improve traffic flow. The framework utilizes a deep neural network to approximate the Q-function, enabling the agent to learn complex traffic patterns and adapt its control strategy accordingly. The effectiveness of the proposed approach is evaluated through simulations, demonstrating its potential to outperform conventional control methods. The core claim of this work is to leverage DRL algorithms to optimize traffic flow and reduce congestion. The central mechanism involves modeling traffic signal control as a DRL problem and utilizing a deep neural network to learn optimal control policies. This represents a novel approach to traffic management, aiming to enhance optimization efficiency.