A Hierarchical Reinforcement Learning Framework for Multi-Agent Cooperative Maneuver Interception in Dynamic Environments by Qinlong Huang, Yasong Luo, Zhong Liu, Jiawei Xia, Ming Chang, Jiaqi Li

“…To address the challenges of real-time decision-making and resource optimization in multi-agent cooperative interception tasks within dynamic environments, this paper proposes a hierarchical framework for reinforcement learning-based interception algorithm (HFRL-IA). …”
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A cooperative jamming decision-making method based on multi-agent reinforcement learning by Bingchen Cai, Haoran Li, Naimin Zhang, Mingyu Cao, Han Yu

“…Abstract Electromagnetic jamming is a critical countermeasure in defense interception scenarios. This paper addresses the complex electromagnetic game involving multiple active jammers and radar systems by proposing a multi-agent reinforcement learning-based cooperative jamming decision-making method (MA-CJD). …”
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ResilioMate: A Resilient Multi-Agent Task Executing Framework for Enhancing Small Language Models by Yubing Xiong, Mingrui Huang, Xuechen Liang, Meiling Tao

“…ResilioMate accomplishes three critical improvements: 1) The 1.8B LeptoConnect model attains 81.6% of GPT-4’s performance in knowledge graph construction through parameter-efficient fine-tuning with structured weight matrices; 2) LeptoConnect-7B achieves a score of 41.3 in database operations, compared to GPT-4’s 32.0, through collaborative cognitive load allocation; and 3) A bias-interception network effectively suppresses adversarial propagation while achieving code correction performance of ROUGE-L’s 42.86. …”
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A Class of Perimeter Defense Strategies Based on Priority Path Planning by Shuang Zhang, Chengqian Yang, Shiwei Lin, Bomin Huang

“…This paper investigates perimeter defense strategies for multi-agent systems. Considering the complex scenario with multiple obstacles in the mission environment, a defense strategy based on prioritized path planning is proposed in this paper. …”
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Exploring the possibilities of MADDPG for UAV swarm control by simulating in Pac-Man environment by Artem Novikov, Sergiy Yakovlev, Ivan Gushchin

“…Future research should focus on comparing the effectiveness of MADDPG-trained models with multi-agent algorithms, such as Expectimax, Alpha-Beta Pruning, and Monte Carlo Tree Search (MCTS), to further understand the advantages and limitations of learning-based approaches compared with traditional decision-making methods in collaborative and adversarial UAV operations. …”
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