Research and Reflection on Intelligent Guidance Based on Deep Reinforcement Learning

doi:10.3969/j.issn.1009-086x.2026.01.007

Abstract

Abstract:

In light of the increasingly multidimensional and complex combat environment， the form of warfare has also undergone a profound transformation. With the continuous development of artificial intelligence technology， its influence in various fields keeps growing. This paper provided a comprehensive overview of the principles and evolution of reinforcement learning， and analyzed the application of deep reinforcement learning in the field of intelligent guidance. It further summarized key technologies within intelligent guidance， and based on current research progress， offered a detailed examination of existing challenges and the implications of AI. This analysis aims to provide insights and directions for the advancement of intelligent guidance.

Key words: intelligent guidance, deep reinforcement learning, guidance law, artificial intelligence, reward function

摘要：

在当前战争环境多元化和复杂化的情况下，战争形态也经历了重大的转变。随着当前人工智能技术的不断发展，其在各个领域的影响力也不断提升。对强化学习的原理以及发展进行了全面的阐述，并且对于深度强化学习在智能制导领域的应用进行了分析；对于智能制导领域的关键技术进行全面的总结，并基于当前智能制导的研究进展，对于当前存在的问题挑战以及智能所带来的影响进行了详细分析，为智能制导的发展提供借鉴和指导。

关键词: 智能制导, 深度强化学习, 制导律, 人工智能, 奖励函数

CLC Number:

TJ765.3

Wei GUO, Yuan CHANG, Fang CHENG, Qingyun WANG, Chong WANG. Research and Reflection on Intelligent Guidance Based on Deep Reinforcement Learning[J]. Modern Defense Technology, 2026, 54(1): 73-84.

郭威, 常远, 程芳, 王清云, 王冲. 基于深度强化学习智能制导的研究思考[J]. 现代防御技术, 2026, 54(1): 73-84.

Figures/Tables 8

Fig. 1 DRL based on value function

Fig. 2 DRL based on policy gradient

Fig. 3 Classification and research directions of classical guidance laws

Fig. 4 Relationship of agent and interaitre enviornment

Fig. 5 Offline training model

Fig. 6 State space parameters

Fig. 7 Reward types

Fig. 8 Migration from simulation domain to real domain

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