A Multi-agent Reinforcement Learning Framework for Simulating Asymmetric UAV Swarm Combat

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A Multi-agent Reinforcement Learning Framework for Simulating Asymmetric UAV Swarm Combat : Proceedings - 2025 7th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2025. / Malafeyev, O.; Zaitseva, I.; Zhang, K.; Kuleshova, L.; Zakharova, N.; Zakharov, V.

2025. 428-433 Paper presented at 7th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA), Lipetsk, Russian Federation.

Research output: Contribution to conference › Paper › peer-review

BibTeX

@conference{39f86514f3b4443e90fc9e3b4a9791b4,

title = "A Multi-agent Reinforcement Learning Framework for Simulating Asymmetric UAV Swarm Combat: Proceedings - 2025 7th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2025",

abstract = "This paper introduces a sophisticated 2D simulation framework designed for evaluating Unmanned Aerial Vehicle (UAV) swarm tactics in a competitive multi-agent environment. The simulation features two opposing teams, Blue and Red, each composed of heterogeneous drones with distinct attributes (speed, endurance, firepower, defense, etc.). The core objective revolves around neutralizing the opponent's {"}General{"}(G-type) drone or achieving a predefined score target. The Blue team employs a hybrid AI strategy combining Ant Colony Optimization (ACO) for scout patrol pathing and a Brown-Robinson game-theoretic approach for a specialized Hunter drone to track and neutralize Evader drones. The Red team utilizes reactive behaviors, including fight-or-flee logic and strategic G-drone positioning, with a focus on assaulting the Blue team's designated operational zone. {\textcopyright} 2025 IEEE.",

keywords = "Ant Colony Optimization, Artificial Intelligence, AV Swarm, Brown-Robinson Algorithm, Game Theory, Multi-Agent Systems, Reinforcement Learning, Simulation, Aircraft detection, Antennas, Computation theory, Distributed computer systems, Drones, Fighter aircraft, Intelligent agents, Learning algorithms, Multi agent systems, Swarm intelligence, Target drones, Aerial vehicle, Ant colonies, AV swarm, Brown-robinson algorithm, Colony optimization, Multi-agent reinforcement learning, Multiagent systems (MASs), Reinforcement learnings, Robinson, Ant colony optimization, Game theory, Reinforcement learning",

author = "O. Malafeyev and I. Zaitseva and K. Zhang and L. Kuleshova and N. Zakharova and V. Zakharov",

note = "Export Date: 23 March 2026; Cited By: 0; Correspondence Address: O. Malafeyev; Saint-Petersburg State University, Department of Modelling in Social and Economical Systems, St. Petersburg, Russian Federation; email: o.malafeev@spbu.ru; Conference name: 7th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2025; Conference date: 12 November 2025 through 14 November 2025; Conference code: 218610; null ; Conference date: 12-11-2025 Through 14-11-2025",

year = "2025",

doi = "10.1109/SUMMA68668.2025.11302288",

language = "Английский",

pages = "428--433",

}

RIS

TY - CONF

T1 - A Multi-agent Reinforcement Learning Framework for Simulating Asymmetric UAV Swarm Combat

AU - Malafeyev, O.

AU - Zaitseva, I.

AU - Zhang, K.

AU - Kuleshova, L.

AU - Zakharova, N.

AU - Zakharov, V.

N1 - Export Date: 23 March 2026; Cited By: 0; Correspondence Address: O. Malafeyev; Saint-Petersburg State University, Department of Modelling in Social and Economical Systems, St. Petersburg, Russian Federation; email: o.malafeev@spbu.ru; Conference name: 7th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2025; Conference date: 12 November 2025 through 14 November 2025; Conference code: 218610

PY - 2025

Y1 - 2025

N2 - This paper introduces a sophisticated 2D simulation framework designed for evaluating Unmanned Aerial Vehicle (UAV) swarm tactics in a competitive multi-agent environment. The simulation features two opposing teams, Blue and Red, each composed of heterogeneous drones with distinct attributes (speed, endurance, firepower, defense, etc.). The core objective revolves around neutralizing the opponent's "General"(G-type) drone or achieving a predefined score target. The Blue team employs a hybrid AI strategy combining Ant Colony Optimization (ACO) for scout patrol pathing and a Brown-Robinson game-theoretic approach for a specialized Hunter drone to track and neutralize Evader drones. The Red team utilizes reactive behaviors, including fight-or-flee logic and strategic G-drone positioning, with a focus on assaulting the Blue team's designated operational zone. © 2025 IEEE.

AB - This paper introduces a sophisticated 2D simulation framework designed for evaluating Unmanned Aerial Vehicle (UAV) swarm tactics in a competitive multi-agent environment. The simulation features two opposing teams, Blue and Red, each composed of heterogeneous drones with distinct attributes (speed, endurance, firepower, defense, etc.). The core objective revolves around neutralizing the opponent's "General"(G-type) drone or achieving a predefined score target. The Blue team employs a hybrid AI strategy combining Ant Colony Optimization (ACO) for scout patrol pathing and a Brown-Robinson game-theoretic approach for a specialized Hunter drone to track and neutralize Evader drones. The Red team utilizes reactive behaviors, including fight-or-flee logic and strategic G-drone positioning, with a focus on assaulting the Blue team's designated operational zone. © 2025 IEEE.

KW - Ant Colony Optimization

KW - Artificial Intelligence

KW - AV Swarm

KW - Brown-Robinson Algorithm

KW - Game Theory

KW - Multi-Agent Systems

KW - Reinforcement Learning

KW - Simulation

KW - Aircraft detection

KW - Antennas

KW - Computation theory

KW - Distributed computer systems

KW - Drones

KW - Fighter aircraft

KW - Intelligent agents

KW - Learning algorithms

KW - Multi agent systems

KW - Swarm intelligence

KW - Target drones

KW - Aerial vehicle

KW - Ant colonies

KW - AV swarm

KW - Brown-robinson algorithm

KW - Colony optimization

KW - Multi-agent reinforcement learning

KW - Multiagent systems (MASs)

KW - Reinforcement learnings

KW - Robinson

KW - Ant colony optimization

KW - Game theory

KW - Reinforcement learning

U2 - 10.1109/SUMMA68668.2025.11302288

DO - 10.1109/SUMMA68668.2025.11302288

M3 - материалы

SP - 428

EP - 433

Y2 - 12 November 2025 through 14 November 2025

ER -

ID: 150936286

A Multi-agent Reinforcement Learning Framework for Simulating Asymmetric UAV Swarm Combat: Proceedings - 2025 7th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2025

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