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

• URL: http://arxiv.org/abs/2603.00974v1
• Date: Sun, 01 Mar 2026 08:05:32 GMT
• Title: Intent-Context Synergy Reinforcement Learning for Autonomous UAV Decision-Making in Air Combat
• Authors: Jiahao Fu, Feng Yang,
• Abstract summary: This paper proposes an Intent-Context Synergy Reinforcement Learning (ICS-RL) framework for autonomous UAV infiltration in contested environments.<n>An LSTM-based Intent Prediction Module forecasts the future trajectories of hostile units, transforming the decision paradigm from reactive avoidance to proactive planning.<n>A Context-Analysis Synergy Mechanism decomposes the mission into hierarchical sub-tasks (safe cruise, stealth planning, and hostile breakthrough)<n>A dynamic switching controller based on Max-Advantage values seamlessly integrates these agents, allowing the UAV to adaptively select the optimal policy without hard-coded rules.
• Score: 2.9612776591672443
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 these agents, allowing the UAV to adaptively select the optimal policy without hard-coded rules. Extensive simulations demonstrate that ICS-RL significantly outperforms baselines (Standard DDQN) and traditional methods (PSO, Game Theory). The proposed method achieves a mission success rate of 88\% and reduces the average exposure frequency to 0.24 per episode, validating its superiority in ensuring robust and stealthy penetration in high-dynamic scenarios.

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