摘 要
随着人工智能技术的快速发展，智能决策系统在复杂环境下的应用需求日益增长，而强化学习作为实现自主决策的核心方法之一，为解决动态、不确定问题提供了新的思路本研究旨在设计并实现一种基于强化学习的智能决策系统，以应对复杂环境中的多目标优化与动态适应问题通过引入深度强化学习算法，结合神经网络的非线性拟合能力与强化学习的策略优化特性，提出了一种改进的深度Q网络模型该模型通过优化经验回放机制和目标网络更新策略，显著提升了系统的收敛速度与稳定性同时，针对传统强化学习算法在高维状态空间中效率较低的问题，本研究提出了一种基于注意力机制的状态特征提取方法，有效降低了计算复杂度并在实际应用场景中验证了系统的性能。
关键词：强化学习 深度Q网络 注意力机制
Abstract
With the rapid development of artificial intelligence technology, The growing demand for intelligent decision-making systems in complex environments, And reinforcement learning, as one of the core methods to realize independent decision-making, The present study aims to design and implement an intelligent decision-making system based on reinforcement learning, In response to the multi-ob jective optimization and dynamic adaptation problems in complex environments, by introducing deep reinforcement learning algorithms, Combining the nonlinear fitting ability of neural networks with the strategy optimization properties of reinforcement learning, Proposed an improved deep Q network model by optimizing the empirical replay mechanism and the target network updating strategy, Significantly improves the convergence rate and stability of the system, To address the problem that traditional reinforcement learning algorithms are less efficient in the high-dimensional state space, This study presents a state feature extraction method based on the attention mechanism, Effectively reduce the computational complexity and verify the performance of the system in practical application scenarios.
Keyword:Reinforcement Learning Deep Q Network Attention Mechanism
目 录
引言 1
1强化学习理论基础研究 1
1.1强化学习基本概念 1
1.2马尔可夫决策过程分析 2
1.3奖励函数设计原则 2
1.4算法收敛性探讨 3
2智能决策系统需求分析 3
2.1决策系统的应用场景 3
2.2用户需求与功能定义 4
2.3数据特征与环境建模 4
2.4性能指标体系构建 4
3系统架构设计与实现 5
3.1整体架构设计思路 5
3.2强化学习模块开发 5
3.3数据处理与预训练流程 6
3.4实时决策机制实现 6
4实验验证与性能评估 7
4.1实验环境搭建与配置 7
4.2对比实验设计与分析 7
4.3性能优化策略研究 8
4.4应用案例效果评估 8
结论 8
参考文献 10
致谢 11
