[2603.00974] Intent-Context Synergy Reinforcement Learning for Autonomous UAV Decision-Making in Air Combat

Computer Science > Machine Learning arXiv:2603.00974 (cs) [Submitted on 1 Mar 2026] Title:Intent-Context Synergy Reinforcement Learning for Autonomous UAV Decision-Making in Air Combat Authors:Jiahao Fu, Feng Yang View a PDF of the paper titled Intent-Context Synergy Reinforcement Learning for Autonomous UAV Decision-Making in Air Combat, by Jiahao Fu and 1 other authors View PDF HTML (experimental) Abstract:Autonomous UAV infiltration in dynamic contested environments remains a significant challenge due to the partially observable nature of threats and the conflicting objectives of mission efficiency versus survivability. Traditional Reinforcement Learning (RL) approaches often suffer from myopic decision-making and struggle to balance these trade-offs in real-time. To address these limitations, this paper proposes an Intent-Context Synergy Reinforcement Learning (ICS-RL) framework. The framework introduces two core innovations: (1) An LSTM-based Intent Prediction Module that forecasts the future trajectories of hostile units, transforming the decision paradigm from reactive avoidance to proactive planning via state augmentation; (2) A Context-Analysis Synergy Mechanism that decomposes the mission into hierarchical sub-tasks (safe cruise, stealth planning, and hostile breakthrough). We design a heterogeneous ensemble of Dueling DQN agents, each specialized in a specific tactical context. A dynamic switching controller based on Max-Advantage values seamlessly integrates th...