Optimization of the Stand Structure in Secondary Forests of Pinus yunnanensis Based on Deep Reinforcement Learning

This study proposes a multi-objective stand structure optimization scheme based on deep reinforcement learning, demonstrating the strengths of deep reinforcement learning in solving multi-objective optimization problems and providing innovative insights for sustainable forest management. Using the Pinus yunnanensis secondary forest in Southwest China as the research subject, we established a stand structure optimization model with stand spatial structure indexes as the optimization objectives and non-spatial structure indexes as the constraints. We optimized the stand structure by combining deep reinforcement learning with three tree-felling decisions: random selection, tree homogeneity index, and spatial competition. Simulated cutting experiments were conducted on circular plots (P1–P5) using deep reinforcement learning and reinforcement learning. The initial objective function values of all plots (0.2950, 0.2954, 0.3445, 0.3010, 0.3168) were effectively improved. The maximum objective function values after optimization by the deep reinforcement learning schemes (0.3815, 0.3701, 0.4301, 0.4599, 0.3689) were significantly better than those achieved by the reinforcement learning schemes (0.3394, 0.3579, 0.3986, 0.4321, 0.3556). Among these, the optimization scheme combining random selection and deep reinforcement learning showed the greatest average improvement across the five plots (29.73%), with its enhancement of the objective function value significantly surpassing that of other optimization schemes. This study applies deep reinforcement learning to stand structure optimization, proposing a new approach to solving multi-objective optimization problems in stand structure and providing a reference for forest health management in Southwest China.