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

Jiahao Fu, Feng Yang

TL;DR

The proposed Intent-Context Synergy Reinforcement Learning framework 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.

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.

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

TL;DR

The proposed Intent-Context Synergy Reinforcement Learning framework 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.

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.
Paper Structure (33 sections, 24 equations, 10 figures, 2 tables)

This paper contains 33 sections, 24 equations, 10 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Problem Description
  3. Path length cost
  4. Threats cost
  5. Theoretical foundation
  6. UAV kinematics model
  7. RL For UAV
  8. Method
  9. MDP Formulation
  10. State Space and Action Space
  11. Reward Space
  12. Intent Analysis
  13. Historical Trajectory Representation
  14. Temporal Feature Extraction
  15. Supervised Training Objective
  16. ...and 18 more sections

Figures (10)

  • Figure 1: Mission Scenario Diagram.
  • Figure 2: UAV Kinematics model.
  • Figure 3: Fundamental framework of reinforcement learning.
  • Figure 4: The structure diagram of air combat decision model.
  • Figure 5: Comparison of cumulative reward curves between CA-DDQN and Standard DDQN.
  • ...and 5 more figures