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

TL;DR

This paper introduces ICS-RL, a novel reinforcement learning framework for autonomous UAV decision-making in contested air combat, combining intent prediction and hierarchical context analysis to improve mission success and survivability.

Contribution

The paper presents a new ICS-RL framework with intent prediction and context synergy, enabling proactive planning and adaptive policy switching for UAVs in complex environments.

Findings

Achieves 88% mission success rate in simulations

Reduces exposure frequency to 0.24 per episode

Outperforms baseline RL and traditional methods

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.