Intelligent decision for joint operations based on improved proximal policy optimization

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.