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The Evolution and Optimization of Game AI: From Rule-Driven to Deep Reinforcement Learning
- 1 Beijing Institute of Technology, Beijing, China
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Abstract
The evolution of game artificial intelligence (AI) from rule-driven systems to deep reinforcement learning (DRL) frameworks has revolutionized player engagement and game development. This review systematically examines the developmental trajectory of game AI, identifying key challenges at each stage: Early rule-based architectures, while reliable in predictable environments, suffered from inflexibility and manual tuning requirements; modern DRL models, despite enabling autonomous strategy learning, face prohibitive computational costs, data inefficiency, and limited cross-genre generalization. Through a comprehensive analysis of case studies—including Super Mario Bros., StarCraft, OpenAI’s Dota 2 AI, and Minecraft’s Voyager AI—this paper highlights performance bottlenecks and emerging solutions. Hybrid approaches integrating lightweight neural networks with symbolic logic, multi-sensory perception systems, and adaptive reward mechanisms enhance adaptability while reducing computational demands. Key innovations, such as cross-game knowledge transfer and dynamic priority adjustment, demonstrate significant efficiency gains, enabling AI to tackle unseen scenarios with reduced hardware dependency. However, sustainability concerns, such as the 18.7 MWh energy consumption per training session, underscore the need for energy-conscious algorithms. The study concludes that balancing AI’s expanding capabilities with ethical considerations—such as environmental impact and accountability—is critical for future advancements. Beyond gaming, these technologies hold transformative potential in fields like virtual training and adaptive education, provided they maintain a harmonious integration of control, adaptability, and ethical guardrails.
Keywords
Game Artificial Intelligence, Rule-Based Systems, Deep Reinforcement Learning, Hybrid AI Approaches, Adaptive Learning
[1]. Cai Xinzhang, "Wisdom of life across life issues: Life and death education in National Sports University" ,Student Affairs-Theory and Practice, vol. 53, no. 2, pp. 72-77, 2014.
[2]. Liu Yifan, "Analysis of the application of artificial intelligence in game development. Digital design" , CG WORLD, vol. 8, no. 7, pp. 86-86, 2019.
[3]. Wang Feiyue, "Artificial intelligence wins in multi-role games", Chinese Science Foundation, vol. 34, no. 2, pp. 85-86, 2020.
[4]. Cao Kunze, "Artificial intelligence and its application in the game field", Science and Technology Communication, vol. 257, no. 8, pp. 162-163, 2020.
[5]. Zheng Xin and Zhang Jing, "Educational games in the era of artificial intelligence: development opportunities and trends", Digital Education, vol. 31, no. 1, pp. 33-37, 2020.
[6]. Wang Yuxuan, "Analysis on the development and application of game artificial intelligence", Science and Technology Communication, vol. 11, no. 2, pp. 141-142, 2019.
[7]. Li Kun, Li Ping and Li Libo, "Design and Implementation of MOBA Game Artificial Intelligence", Computer and Information Technology, vol. 154, no. 4, pp. 12-15, 2018.
[8]. Wang Shiying, "The simplified version of “artificial intelligence” is rejected in management", School of Business, vol. 1, no. 1, pp. 28-28, 2015.
[9]. Feng Zeyu and Zhao Erhu, "A survey of high school students’ awareness and needs of artificial intelligence", Electronic World, vol. 552, no. 18, pp. 34-35, 2018.
[10]. Ji Zili and Wang Wenhua, "Strategic planning for the development of military applications of artificial intelligence for world military powers", Military Digest, vol. 473, no. 17, pp. 9-12, 2020.
[11]. Li Haoyuan, "Talking about key technologies and applications of game artificial intelligence", Digital World, vol. 151, no. 5, pp. 451-451, 2018.
[12]. Vinyals, O., Babuschkin, I., Czarnecki, W.M. et al. Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature 575, 350–354 (2019). https://doi.org/10.1038/s41586-019-1724-z.
[13]. Brockman, G., et al. (2016). Proximal Policy Optimization Algorithms. arXiv:1707.06347.
[14]. ]Berner, C., et al. (2019). Dota 2 with Large Scale Deep Reinforcement Learning. arXiv:1912.06680.
[15]. OpenAI Five Blog(2018) ,“OpenAI Five Benchmark: Results”. https://openai.com/index/openai-five/
[16]. Wang G, Xie Y, Jiang Y, et al. Voyager: An open-ended embodied agent with large language models[J]. arXiv preprint arXiv:2305.16291, 2023.
[17]. Radford A, Kim J W, Hallacy C, et al. Learning transferable visual models from natural language supervision[C]//International conference on machine learning. PmLR, 2021: 8748-8763.
[18]. Radford A, Kim J W, Hallacy C, et al. Learning transferable visual models from natural language supervision[C]//International conference on machine learning. PmLR, 2021: 8748-8763.
Cite this article
Zheng,Q. (2025). The Evolution and Optimization of Game AI: From Rule-Driven to Deep Reinforcement Learning. Applied and Computational Engineering,150,77-82.
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