Dynamic optimization of stand structure in

Introduction: The rational structure of forest stands plays a crucial role in maintaining ecosystem functions, enhancing community stability, and ensuring sustainable management. Although progress has been made in stand structure optimization, most existing studies focus on static improvements and fail to adequately capture the dynamic nature of stand development. In addition, commonly used heuristic and traditional methods often suffer from limitations in computational efficiency and generalization ability. Methods: To address these challenges, this study explores the potential and advantages of multi-agent deep reinforcement learning in forest management, offering innovative insights and methods for achieving sustainable forest ecosystem management. Using the secondary forests of Results: The results indicate that multi agent deep reinforcement learning consistently outperformed multi agent reinforcement learning across all plots. For the initial objective function values of each plot (0.3501, 0.3799, 0.3982, 0.3344, 0.4294), the optimized results obtained through multi agent deep reinforcement learning (0.5378, 0.5861, 0.5860, 0.5130, 0.6034) were significantly superior to the maximum objective function values achieved by multi agent reinforcement learning (0.5302, 0.5369, 0.5766, 0.5014, 0.5906). Furthermore, the dynamic optimization results incorporating structure prediction demonstrate that all plots progressively approached an ideal stand condition over multiple optimization cycles (0.5718, 0.6101, 0.6455, 0.5863, 0.6210), leading to a more balanced stand structure and improved long-term stability. Discussion: This study proposes a novel stand structure optimization method that integrates multi agent deep reinforcement learning with structure prediction, providing theoretical support and practical guidance for the sustainable management of