Fighter Jet Navigation and Combat using Deep Reinforcement Learning with Explainable AI
On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control
Abstract (arXiv)
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: hrefhttps://github.com/swatikar95/Autonomous-Fighter-Jet-Navigation-and-CombatProject GitHub Link.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 1 of its 9 references that are in the map. First posted on arXiv 19 Feb 2025.
Builds on
1 of 9 references.
References · 1
- 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026Perfecting Aircraft Maneuvers with Reinforcement Learning
- 2025A Hierarchical Hybrid AI Approach: Integrating Deep Reinforcement Learning and Scripted Agents in Combat Simulations
- 2025Explaining Strategic Decisions in Multi-Agent Reinforcement Learning for Aerial Combat Tactics
- 2025Enhancing Aerial Combat Tactics through Hierarchical Multi-Agent Reinforcement Learning
- 2024Scaling Intelligent Agents in Combat Simulations for Wargaming
- 2024Mastering the Digital Art of War: Developing Intelligent Combat Simulation Agents for Wargaming Using Hierarchical Reinforcement Learning
- 2024Interpretable DRL-based Maneuver Decision of UCAV Dogfight
- 2025Light Aircraft Game : Basic Implementation and training results analysis