用深度强化学习训练智能战机，能自主导航、攻击或躲避敌人。
Fighter Jet Navigation and Combat using Deep Reinforcement Learning with Explainable AI
- 通过深度强化学习让战机在模拟环境中完成多目标任务。
- 任务完成率超80%，决策能力显著。
- 结合反事实分析解释动作选择，提升可解释性。
本文在自定义的Pygame仿真环境中开发了一种基于人工智能的战斗机智能体，通过深度强化学习（DRL）解决多目标任务。战机需高效导航、抵达目标，并选择性地攻击或规避敌方。通过优化的奖励函数与超参数，平衡各项任务目标，提升学习效率。实验结果显示任务完成率超过80%，证明了其有效决策能力。为增强透明度，采用反事实分析比较实际选择动作与替代动作的奖励值，揭示决策逻辑。该研究展示了深度强化学习在复杂多目标场景中结合可解释AI的潜力。
原文摘要 · Abstract (English)
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 page is available at: \href{https://github.com/swatikar95/Autonomous-Fighter-Jet-Navigation-and-Combat}{Project GitHub Link}.
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