Dynamic Optimization of Stand Structure in Pinus yunnanensis Secondary Forests Based on Deep Reinforcement Learning and Structural Prediction

Jian Zhao¹Jianming Wang^1*Jiting Yin²Yuling Chen³Baoguo Wu⁴

• ¹Dali University, Dali, Yunnan, China
• ²Dali Forestry and Grassland Science Research Institute, Dali, China
• ³Peking University, Beijing, Beijing Municipality, China
• ⁴Beijing Forestry University, Beijing, Beijing, China

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. 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 Pinus yunnanensis in southwest China as the research subject, we constructed an objective function and constraints based on spatial and non-spatial structure indexes. Selective harvesting and replanting were employed as optimization measures, and experiments were conducted on five circular plots to compare the performance of multi-agent deep reinforcement learning with that of multi-agent reinforcement learning. To account for the dynamic characteristics of stand structure, we further integrated structure prediction with multi-agent deep reinforcement learning for dynamic optimization across the five plots. 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 Jian Zhao et al. Dynamic Stand Optimization with MADRL and Prediction (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. 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 Pinus yunnanensis secondary forests.