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<p>To tackle challenges such as convergence difficulties and suboptimal performance in the application of reinforcement learning to intelligent decision-making for joint operations, this study introduces an enhanced decision-making approach for joint operations utilizing an improved Proximal Policy Optimization (PPO) algorithm. We propose a structured intelligent decision-making model designed to execute decision-making functions effectively. The strategy loss mechanism is improved by constraining the upper limit of the strategy loss function. Furthermore, a priority sampling mechanism, is developed to assess sample values, thereby enhancing the efficiency of sampling training. Additionally, a network structure facilitating distributed interaction and centralized learning is designed to expedite the training process. The proposed method is then applied to a joint operations simulation platform for intelligent decision-making. Simulation results demonstrate that our algorithm successfully addresses the aforementioned issues, enabling autonomous decisions based on battlefield dynamics, and ultimately leading to victory.</p>

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Intelligent decision for joint operations based on improved proximal policy optimization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月19日

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  • Chen Li,
  • Wenhan Dong,
  • Lei He,
  • Ming Cai,
  • Dafei Wang

摘要

To tackle challenges such as convergence difficulties and suboptimal performance in the application of reinforcement learning to intelligent decision-making for joint operations, this study introduces an enhanced decision-making approach for joint operations utilizing an improved Proximal Policy Optimization (PPO) algorithm. We propose a structured intelligent decision-making model designed to execute decision-making functions effectively. The strategy loss mechanism is improved by constraining the upper limit of the strategy loss function. Furthermore, a priority sampling mechanism, is developed to assess sample values, thereby enhancing the efficiency of sampling training. Additionally, a network structure facilitating distributed interaction and centralized learning is designed to expedite the training process. The proposed method is then applied to a joint operations simulation platform for intelligent decision-making. Simulation results demonstrate that our algorithm successfully addresses the aforementioned issues, enabling autonomous decisions based on battlefield dynamics, and ultimately leading to victory.