2-D Air Combat Maneuver Decision Using Reinforcement Learning

Tasbas, Ahmet Semih; Aydinli, Sevket Utku

doi:10.1109/iceet53442.2021.9659753

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Some scholars have noticed the potential of deep reinforcement learning for application in air combat decision-making and have conducted some related research. Zhang et al [46] propose a heuristic Q-Network method that integrates expert experience and uses expert experience as a heuristic signal to guide the search process. Based on this, Zhong et al [47] used visual sensor information as input data and a DQN network to achieve autonomous decision-making for air combat maneuvers in 3D space.…”

Section: Advantages and Challenges Of Deep Reinforcementmentioning

confidence: 99%

A Review of Deep Reinforcement Learning Methods and Military Application Research

Wang

Liang

et al. 2023

Mathematical Problems in Engineering

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In the area of artificial intelligence, deep reinforcement learning has grown in significance. It has accomplished extraordinary feats and offers a fresh approach to previously challenging challenges, such as controlling a robotic arm and discovering game strategies. The two primary categories of deep reinforcement learning methods—deep reinforcement learning based on value function and deep reinforcement learning based on policy gradient—are initially explained in this study. The limitations of current approaches and the difficulties faced by deep reinforcement learning methods in related domains are further sorted out, and then the future application directions of deep reinforcement learning methods in the military sphere are examined. Finally, a growing trend for deep reinforcement learning techniques is anticipated in military applications.

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Section: Advantages and Challenges Of Deep Reinforcementmentioning

confidence: 99%