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Intelligent decision for joint operations based on improved proximal policy optimization.

Chen Li1, Wenhan Dong2, Lei He1

  • 1Air Force Engineering University, Xi'an, 710038, China.

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Summary
This summary is machine-generated.

This study enhances joint operations decision-making using an improved Proximal Policy Optimization (PPO) algorithm, overcoming convergence issues for autonomous battlefield success.

Keywords:
Intelligent decision-makingJoint operationsMilitary simulationPolicy lossReward function

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Operations Research

Background:

  • Reinforcement learning (RL) faces challenges in intelligent decision-making for joint operations, including convergence difficulties and suboptimal performance.
  • Existing RL methods require improvements for complex, dynamic battlefield environments.

Purpose of the Study:

  • To introduce an enhanced decision-making approach for joint operations using an improved Proximal Policy Optimization (PPO) algorithm.
  • To address convergence difficulties and suboptimal performance in RL applications for intelligent joint operations.

Main Methods:

  • An improved Proximal Policy Optimization (PPO) algorithm with a constrained strategy loss function.
  • A priority sampling mechanism to assess sample values and enhance training efficiency.
  • A network structure for distributed interaction and centralized learning to expedite training.

Main Results:

  • The enhanced PPO algorithm successfully addresses convergence and performance issues in joint operations decision-making.
  • The proposed method enables autonomous decision-making based on real-time battlefield dynamics.
  • Simulation results indicate a significant improvement in operational effectiveness, leading to successful outcomes.

Conclusions:

  • The developed intelligent decision-making approach significantly improves RL performance in joint operations.
  • The enhanced PPO algorithm provides a robust framework for autonomous decision-making in complex operational environments.
  • This research contributes to advancing AI applications in military strategy and operations.