摘要
随着人工智能技术的迅猛发展，强化学习作为机器学习的重要分支，在处理复杂决策问题方面展现出独特优势。本研究聚焦于强化学习算法及其在游戏领域的应用，旨在探讨其理论基础、优化策略及实际效能。通过对深度Q网络（DQN）、近端策略优化（PPO）等典型算法的深入分析，结合AlphaGo的成功案例，揭示了强化学习在解决非确定性环境下的动态决策问题中的潜力。研究采用实验对比法，以经典游戏为测试平台，验证不同算法的学习效率与收敛性能。结果表明，引入经验回放机制和目标网络的DQN有效解决了相关性样本问题，显著提高了模型稳定性；而PPO通过限制策略更新幅度，避免了训练过程中的性能崩溃。创新点在于提出了一种融合多智能体协作与对抗训练的新框架，该框架不仅增强了个体智能体的适应能力，还促进了群体智能的涌现。此外，针对游戏场景中高维状态空间和稀疏奖励函数的挑战，设计了基于注意力机制的状态表示方法，大幅提升了算法在复杂环境下的泛化能力。研究表明，强化学习算法在游戏领域取得了突破性进展，未来有望拓展至更多实际应用场景，为智能系统的发展提供新的思路和技术支持。
关键词：强化学习；深度Q网络；近端策略优化；多智能体协作；注意力机制状态表示
Abstract
With the rapid advancement of artificial intelligence technologies, reinforcement learning (RL), as a crucial branch of machine learning, has demonstrated unique advantages in addressing complex decision-making problems. This study focuses on RL algorithms and their applications in the gaming domain, aiming to explore their theoretical foundations, optimization strategies, and practical efficacy. By conducting an in-depth analysis of representative algorithms such as Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), alongside the success case of AlphaGo, this research reveals the potential of RL in solving dynamic decision-making problems under non-deterministic environments. The study employs experimental comparison methods using classic games as test platforms to verify the learning efficiency and convergence performance of different algorithms. Results indicate that DQN, by incorporating experience replay mechanisms and target networks, effectively addresses the issue of correlated samples and significantly enhances model stability; whereas PPO avoids performance collapse during training by constraining the extent of policy updates. An innovation of this study is the proposal of a new fr amework that integrates multi-agent cooperation and adversarial training, which not only strengthens individual agents' adaptability but also promotes the emergence of collective intelligence. Additionally, in response to the challenges posed by high-dimensional state spaces and sparse reward functions in gaming scenarios, a state representation method based on attention mechanisms has been designed, substantially improving the algorithm's generalization capability in complex environments. The findings suggest that RL algorithms have achieved breakthrough progress in the gaming field and hold promise for broader practical applications, providing new insights and technical support for the development of intelligent systems.
Keywords：Reinforcement Learning; Deep Q Network; Proximal Policy Optimization; Multi-agent Collaboration; Attention Mechanism State Representation
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状 1
(三) 本文研究方法 2
二、强化学习算法原理 2
(一) 强化学习基本概念 2
(二) 常见强化学习算法 3
(三) 强化学习算法评价指标 3
三、游戏中的强化学习应用 4
(一) 游戏环境特点分析 4
(二) 强化学习在游戏 5
(三) 游戏中强化学习的挑战 5
四、案例分析与展望 6
(一) 典型游戏案例分析 6
(二) 强化学习算法优化方向 7
(三) 未来发展趋势探讨 8
结 论 9
参考文献 10
