摘 要
深度强化学习作为人工智能领域的重要分支，在游戏AI中展现出巨大潜力。随着AlphaGo的成功，研究者们开始探索将深度强化学习应用于不同类型的游戏以提升游戏AI的智能水平。本研究旨在通过引入深度强化学习算法改善游戏AI性能，使其在复杂多变的游戏环境中具备更强适应能力与决策能力。基于此目的，采用深度Q网络（DQN）、近端策略优化（PPO）等典型深度强化学习算法对多种类型游戏进行训练测试。实验选取了经典街霸格斗游戏、星际争霸即时战略游戏以及德州扑克棋牌类游戏作为研究对象，针对不同游戏特点设计特定奖励机制与状态表示方法。结果表明，经过深度强化学习训练后的游戏AI能够有效掌握游戏规则并制定合理策略，在对抗人类玩家时取得了显著进步，部分场景下甚至超越了专业选手水平。创新点在于首次将深度强化学习全面系统地应用于不同类型游戏AI开发，并提出了一套通用框架用于指导后续研究工作。该成果不仅为游戏产业带来了全新发展机遇，也为其他领域智能化发展提供了有益借鉴，证明了深度强化学习在构建高效智能系统方面具有广阔应用前景。
关键词：深度强化学习 游戏AI 深度Q网络
Abstract
Deep reinforcement learning, as a significant branch of artificial intelligence, has demonstrated substantial potential in game AI. Following the success of AlphaGo, researchers have begun to explore the application of deep reinforcement learning in various types of games to enhance the intelligence level of game AI. This study aims to improve game AI performance by introducing deep reinforcement learning algorithms, enabling it to possess stronger adaptability and decision-making capabilities in complex and dynamic gaming environments. For this purpose, typical deep reinforcement learning algorithms such as Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) were employed to train and test multiple types of games. The experiments selected classic Street Fighter fighting games, StarCraft real-time strategy games, and Texas Hold'em poker card games as research subjects, designing specific reward mechanisms and state representation methods according to the characteristics of different games. The results indicate that after training with deep reinforcement learning, the game AI can effectively grasp the rules of the games and formulate reasonable strategies, achieving significant progress when competing against human players and even surpassing professional player levels in some scenarios. The innovation lies in being the first to comprehensively and systematically apply deep reinforcement learning to the development of different types of game AI and proposing a universal fr amework to guide subsequent research. This achievement not only brings new development opportunities for the gaming industry but also provides valuable references for intelligent development in other fields, demonstrating the broad application prospects of deep reinforcement learning in constructing efficient intelligent systems.
Keyword:Deep Reinforcement Learning Game Ai、Deep Q Network Proximal Policy Optimization
目 录
引言 1
1深度强化学习基础理论 1
1.1强化学习基本概念 1
1.2深度学习原理概述 2
1.3深度强化学习框架 2
2游戏AI的需求与挑战 3
2.1游戏 3
2.2游戏 3
2.3深度强化学习的适用性 4
3深度强化学习在游戏中的应用 4
3.1算法选择与优化 5
3.2游戏环境建模 5
3.3实验结果与分析 6
4未来发展方向与展望 6
4.1技术瓶颈与突破 6
4.2多智能体协同 7
4.3应用场景拓展 7
结论 8
参考文献 9
致谢 9
