Improved Monte Carlo tree search (MCTS) formulation with multiple root nodes for discrete sizing optimization of truss structures

This paper proposes a new method for discrete optimum design of truss structures utilizing Monte Carlo tree search (MCTS) with update process, the best reward, accelerating technique, and terminal condition. An improved MCTS formulation with multiple root nodes is developed in this study. Update process means that once a final solution is found, it is used as the initial solution for next search tree. The best reward is used in the backpropagation step. Accelerating technique is introduced by decreasing the width of search tree and reducing maximum number of iterations. The agent is trained to minimize the total structural weight under various constraints until the terminal condition is satisfied. Then, optimal solution is the minimum value of all solutions found by search trees. These numerical examples show that the agent can find optimal solution with low computational cost, stably produces an optimal design, and is suitable for practical engineering problems.