Over-the-Horizon Air Combat Environment Modeling and Deep Reinforcement Learning Application

Wang, Ao; Zhao, Shangwei; Shi, Zhengkang; Wang, Jingcheng

doi:10.1109/docs55193.2022.9967482

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…Because of the sparse nature of the air combat environment, the shaping of the reward function has been a key challenge in the application of reinforcement learning to air combat [23,24]. Piao constructed a high-fidelity air combat simulation environment and proposed a critical air combat event reward-shaping mechanism to reduce episodic win-lose signals [25,26], enabling fast convergence of the training process. The implementation results showed that reinforcement learning can generate a variety of valuable air combat tactical behaviors under beyond-visual-range conditions.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Reinforcement Learning Framework in Geographic Coordination for Air Combat Tactical Pursuit

Chen,

Li,

Yan

et al. 2023

Entropy

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This paper proposes an air combat training framework based on hierarchical reinforcement learning to address the problem of non-convergence in training due to the curse of dimensionality caused by the large state space during air combat tactical pursuit. Using hierarchical reinforcement learning, three-dimensional problems can be transformed into two-dimensional problems, improving training performance compared to other baselines. To further improve the overall learning performance, a meta-learning-based algorithm is established, and the corresponding reward function is designed to further improve the performance of the agent in the air combat tactical chase scenario. The results show that the proposed framework can achieve better performance than the baseline approach.

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Section: Introductionmentioning

confidence: 99%

Hierarchical Reinforcement Learning Framework in Geographic Coordination for Air Combat Tactical Pursuit

Chen,

Li,

Yan

et al. 2023

Entropy

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Joint Situational Assessment‐Hierarchical Decision‐Making Framework for Maneuver Intent Decisions

Chen,

Li,

Yan

et al. 2024

Advanced Intelligent Systems

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Decision‐making in unmanned combat aerial vehicles (UCAVs) presents a multifaceted challenge because of the complexity and dynamics of the flight environment, which leads to hurdles in training convergence, low decision validity, and the dimensionality catastrophe for decision‐making neural networks. A novel framework is proposed to address breaking down the complicated decision issues, which combines the strengths of graph convolutional networks in relation extraction with the ability of hierarchical reinforcement learning. To solve the problem of decision validity under high‐dimensional inputs, the joint framework is applied to the Maneuver Intent's decision, and a maneuver library‐based state space design method is suggested. The joint framework executes adaptable strategies and flight maneuvers to address the issue of training non‐convergence or task failure due to difficult‐to‐obtain reward signals across various scenarios. Then, the recurrent curriculum training and cross‐entropy rewards are designed to train decisions on different sub‐strategies. The experimental evaluation demonstrated more flexibility and adaptability in decision‐making problems under complex tasks compared to rule‐based and reinforcement learning baseline methods. The method proposed in this article provides a novel approach to resolving intricate decision problems, and which has certain theoretical significance and reference value for engineering applications.

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A Multi-UAV Pursuit Strategy Based on Two-Stage Collaborative Search

Ru,

Guan,

et al. 2024

Lecture Notes in Electrical Engineering

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Over-the-Horizon Air Combat Environment Modeling and Deep Reinforcement Learning Application

Cited by 4 publications

References 12 publications

Hierarchical Reinforcement Learning Framework in Geographic Coordination for Air Combat Tactical Pursuit

Hierarchical Reinforcement Learning Framework in Geographic Coordination for Air Combat Tactical Pursuit

Joint Situational Assessment‐Hierarchical Decision‐Making Framework for Maneuver Intent Decisions

A Multi-UAV Pursuit Strategy Based on Two-Stage Collaborative Search

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