This paper presents the development of an Artificial Intelligence (AI) based fighter jet agent within a customized Pygame simulation environment, designed to solve multi-objective tasks via deep reinforcement learning (DRL). The jet's primary objectives include efficiently navigating the environment, reaching a target, and selectively engaging or evading an enemy. A reward function balances these goals while optimized hyperparameters enhance learning efficiency. Results show more than 80 % task completion rate, demonstrating effective decision-making. To enhance transparency, the jet's action choices are analyzed by comparing the rewards of the actual chosen action (factual action) with those of alternate actions (counterfactual actions), providing insights into the decision-making rationale. This study illustrates DRL's potential for multi-objective problem-solving with explainable AI. Project code is available at: https://github.com/swatikar95/Autonomous-Fighter-Jet-Navigation-and-Combat.


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    Title :

    Fighter Jet Navigation and Combat Using Deep Reinforcement Learning With Explainable AI


    Contributors:


    Publication date :

    2025-05-14


    Size :

    1266085 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English