2/19/2024 0 Comments We ii air combat maneuvers pdf![]() ![]() īergdahl J, Gordillo C, Tollmar K, Gisslén L (2021) Augmenting automated game testing with deep reinforcement learning. īayerlein H, Theile M, Caccamo M, Gesbert D (2021) Multi-UAV path planning for wireless data harvesting with deep reinforcement learning. īae J, Jung H, Kim S, Kim S, Kim Y-D (2023) Deep reinforcement learning-based air-to-air combat maneuver generation in a realistic environment. Īzar AT, Koubaa A, Ali Mohamed N, Ibrahim HA, Ibrahim ZF, Kazim M, Ammar A, Benjdira B, Khamis AM, Hameed IA, Casalino G (2021) Drone deep reinforcement learning: a review. Īustin F, Carbone G, Hinz H, Lewis M, Falco M (1991) Game theory for automated maneuvering during air-to-air combat. American Institute Aeronaut Astronautics. Īustin F, Carbone G, Falco M, Hinz H, Lewis M (1987) Automated maneuvering decisions for air-to-air combat. Īrulkumaran K, Deisenroth MP, Brundage M, Bharath AA (2017) Deep reinforcement learning: a brief survey. Īlpdemir MN (2022) Tactical UAV path optimization under radar threat using deep reinforcement learning. ĪlMahamid F, Grolinger K (2022) Autonomous unmanned aerial vehicle navigation using reinforcement learning: a systematic review. 2023–May–21Īkabari S, Menhaj MB, Nikravesh SK (2005) Fuzzy modeling of offensive maneuvers in an air-to-air combat. 2023–May–21Īir combat reinforcement learning. Finally, limitations of the considered model, as well as the possible future research direction for intelligent air combat, are also discussed.Īir Combat Evolution Project Overview. And the associated Python codes are available at /wangyyhhh, thus enabling a quick-start for researchers to build their own ACMD applications by slight modifications. The model establishment, program design, training methods and performance evaluation are described in detail. Then, a maneuver decision-making method based on one-to-one dogfight scenarios is proposed to enable UAV to win short-range air combat. ![]() And special attentions are given to the design of reward function, which is the core of DRL-based ACMD. It starts from the DRL itself and then extents to its application in ACMD. For this reason, this paper first provides a comprehensive literature review to help people grasp a whole picture of this field. However, as an emerging topic, there lacks a systematic review and tutorial. Deep reinforcement learning (DRL), which is suitable for sequential decision-making process, provides a powerful solution tool for air combat maneuver decision-making (ACMD), and hundreds of related research papers have been published in the last five years. During the operation, UAVs are expected to perform agile and safe maneuvers according to the dynamic mission requirement and complicated battlefield environment. Nowadays, various innovative air combat paradigms that rely on unmanned aerial vehicles (UAVs), i.e., UAV swarm and UAV-manned aircraft cooperation, have received great attention worldwide. ![]()
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