Status and Prospect on Deep Reinforcement Learning Decision-Making Methods for Intelligent Air Combat
This paper focuses on the development of modern intelligent air combat decision-making technology,and analyzes the elements and characteristics of intelligent air combat scenarios.It introduces the research status and practical application of existing intelligent air combat decision-making methods,including decision-making methods based on game theory,prior data-driven decision-making method,and decision-making methods based on autonomous learning,and especially focuses on deep reinforcement learning intelligent decision-making methods based on value and strategy.Finally,facing to various challenges of future intelligent air combat and the limitations of traditional deep rein-forcement learning,the paper gives the future development direction of deep reinforcement learning technology in the field of air combat,which are multi-agent intelligent decision-making technology for cluster warfare,efficient intelligent decision-making technology for wide area space-time,and generalized intelligent decision-making technology for complex scenarios.
air combat decision-makingartificial intelligencereinforcement learningintelligent gamecluster warfaredeep learning
万方数据
维普
