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This paper proposes a novel reinforcement learning (RL) algorithm usingimproved Monte Carlo tree search (IMCTS) formulation for discrete optimumdesign of truss structures. IMCTS with multiple root nodes includes updateprocess, the best reward, accelerating technique, and terminal condition.Update process means that once a final solution is found, it is used as theinitial solution for next search tree. The best reward is used in thebackpropagation step. Accelerating technique is introduced by decreasing thewidth of search tree and reducing maximum number of iterations. The agent istrained to minimize the total structural weight under various constraints untilthe terminal condition is satisfied. Then, optimal solution is the minimumvalue of all solutions found by search trees. These numerical examples showthat the agent can find optimal solution with low computational cost, stablyproduces an optimal design, and is suitable for multi-objective structuraloptimization and large-scale structures.

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