摘 要
随着智能交通系统的发展，自动驾驶技术成为当前研究的热点领域。传统基于规则的方法难以应对复杂多变的道路环境，而深度强化学习凭借其强大的自适应能力为解决这一问题提供了新思路。本文旨在探索基于深度强化学习的自动驾驶策略，以实现更加安全、高效的无人驾驶为目标。通过构建模拟驾驶环境，采用深度Q网络算法对车辆行驶过程中的决策进行建模，将道路状况、交通标志等信息作为输入，输出相应的驾驶动作。与以往方法不同，本研究创新性地引入了多智能体协同机制，使多个车辆能够相互协作，共同优化行驶路径，提高了系统的鲁棒性和泛化能力。实验结果表明，在多种复杂场景下，该策略不仅能够有效避免碰撞，还能根据实时路况调整车速和方向，确保行车安全的同时提高了通行效率。此外，模型在未见过的测试环境中同样表现出色，证明了其良好的迁移学习能力。总之，本研究提出的基于深度强化学习的自动驾驶策略为未来智能交通系统的发展提供了新的理论依据和技术支持，具有重要的学术价值和广阔的应用前景。
关键词：深度强化学习；自动驾驶；多智能体协同；深度Q网络；迁移学习能力
Abstract
With the development of intelligent transportation systems, autonomous driving technology has become a focal area of current research. Traditional rule-based methods struggle to cope with the complex and dynamic road environments, whereas deep reinforcement learning offers a novel approach by leveraging its strong adaptive capabilities. This study aims to explore autonomous driving strategies based on deep reinforcement learning, targeting safer and more efficient driverless operation. By constructing a simulated driving environment, this research employs the Deep Q-Network (DQN) algorithm to model decision-making during vehicle operation, using information such as road conditions and traffic signs as inputs and generating corresponding driving actions as outputs. Unlike previous approaches, this study innovatively incorporates a multi-agent cooperation mechanism, enabling multiple vehicles to collaborate in optimizing their travel routes, thereby enhancing the robustness and generalization ability of the system. Experimental results demonstrate that under various complex scenarios, this strategy not only effectively avoids collisions but also adjusts speed and direction according to real-time traffic conditions, ensuring safety while improving traffic efficiency. Moreover, the model performs well in unseen test environments, indicating its excellent transfer learning capability. In conclusion, the deep reinforcement learning-based autonomous driving strategy proposed in this study provides new theoretical foundations and technical support for the future development of intelligent transportation systems, holding significant academic value and broad application prospects.
Keywords：Deep Reinforcement Learning；Autonomous Driving；Multi-agent Collaboration；Deep Q Network；Transfer Learning Ability
目 录
摘 要 I
Abstract II
引 言 1
第一章 深度强化学习基础理论 2
1.1 强化学习基本原理 2
1.2 深度学习框架概述 2
1.3 深度强化学习算法分类 3
第二章 自动驾驶环境建模 5
2.1 驾驶场景数据采集 5
2.2 环境感知与状态表示 5
2.3 动态环境模拟构建 6
第三章 决策策略设计与优化 8
3.1 行为决策模型构建 8
3.2 奖励函数设计原则 8
3.3 策略迭代与收敛性 9
第四章 系统实现与性能评估 11
4.1 实验平台搭建方案 11
4.2 测试场景设计方法 11
4.3 性能指标与结果分析 12
结 论 14
参考文献 15
致 谢 16
