深度强化学习算法在自动驾驶系统中的应用与优化  

Application and Optimization of Deep Reinforcement Learning Algorithm in Auto Drive System

作　　者：张丽丽 ZHANG Lili(Shenzhen Yangang Middle School,Shenzhen Guangdong 518118)

机构地区：[1]深圳市盐港中学,广东深圳518118

出　　处：《软件》2025年第2期178-180,共3页Software

摘　　要：深度强化学习(DRL)结合了深度学习和强化学习的优势,近年来在自动驾驶系统中取得了显著成果。通过智能体与环境的交互,DRL能够有效应对动态复杂的交通环境,实现路径规划、实时决策和多智能体协作等任务。在路径规划、多目标优化和实时决策等方面,DRL展现了强大的应用潜力。针对数据问题、算法稳定性和计算复杂度等挑战,本文提出了迁移学习、经验回放和策略梯度优化等技术。本文旨在探索DRL在自动驾驶系统中的具体应用与优化策略,推动相关领域的技术进步。Deep Reinforcement Learning(DRL)combines the advantages of deep learning and reinforcement learning,achieving significant achievement in autonomous driving systems in recent years.Through the interaction between agents and the environment,DRL can effectively address dynamic and complex traffic scenarios,enabling tasks such as path planning,real-time decision-making,and multi-agent collaboration.DRL demonstrates strong application potential in areas like path planning,multi-objective optimization,and real-time decision-making.To address challenges such as data issues,algorithm stability,and computational complexity,techniques like transfer learning,experience replay,and policy gradient optimization are proposed in this article.This paper aims to explore the specific applications and optimization strategies of DRL in autonomous driving systems,contributing to technological advancement in the field.

关 键 词：深度强化学习 自动驾驶 算法优化 

分 类 号：TB183[一般工业技术]