4.7 Article

Intelligent Game Strategies in Target-Missile-Defender Engagement Using Curriculum-Based Deep Reinforcement Learning

Journal

AEROSPACE
Volume 10, Issue 2, Pages -

Publisher

MDPI
DOI: 10.3390/aerospace10020133

Keywords

target-missile-defender engagement; three-body game; curriculum learning; deep reinforcement learning; intelligent game; active defense

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Intelligent game strategies based on deep reinforcement learning are proposed to address the attack and defense game problem in the target-missile-defender three-body confrontation scenario. The strategies include an attack strategy for attacking missiles and an active defense strategy for the target/defender. Reinforcement learning algorithm is introduced to improve the training purposefulness, and the reward function design considers the action spaces and reward/punishment conditions of attack and defense confrontation. Simulation results show that the missile's attack strategy can maneuver according to the battlefield situation and successfully hit the target, while the active defense strategy enables the less capable target/defender to defend against missiles with superior maneuverability.
Aiming at the attack and defense game problem in the target-missile-defender three-body confrontation scenario, intelligent game strategies based on deep reinforcement learning are proposed, including an attack strategy applicable to attacking missiles and active defense strategy applicable to a target/defender. First, based on the classical three-body adversarial research, the reinforcement learning algorithm is introduced to improve the purposefulness of the algorithm training. The action spaces the reward and punishment conditions of both attack and defense confrontation are considered in the reward function design. Through the analysis of the sign of the action space and design of the reward function in the adversarial form, the combat requirements can be satisfied in both the missile and target/defender training. Then, a curriculum-based deep reinforcement learning algorithm is applied to train the agents and a convergent game strategy is obtained. The simulation results show that the attack strategy of the missile can maneuver according to the battlefield situation and can successfully hit the target after avoiding the defender. The active defense strategy enables the less capable target/defender to achieve the effect similar to a network adversarial attack on the missile agent, shielding targets from attack against missiles with superior maneuverability on the battlefield.

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