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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2023
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| Accès en ligne: | https://arxiv.org/abs/2309.06045 |
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| _version_ | 1866914902986194944 |
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| author | Ko, Fu-Yao Suzuki, Katsuyuki Yonekura, Kazuo |
| author_facet | Ko, Fu-Yao Suzuki, Katsuyuki Yonekura, Kazuo |
| contents | This paper proposes a novel reinforcement learning (RL) algorithm using improved Monte Carlo tree search (IMCTS) formulation for discrete optimum design of truss structures. IMCTS with multiple root nodes includes update process, the best reward, accelerating technique, and terminal condition. 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 multi-objective structural optimization and large-scale structures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_06045 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Improved Monte Carlo tree search formulation with multiple root nodes for discrete sizing optimization of truss structures Ko, Fu-Yao Suzuki, Katsuyuki Yonekura, Kazuo Artificial Intelligence Numerical Analysis This paper proposes a novel reinforcement learning (RL) algorithm using improved Monte Carlo tree search (IMCTS) formulation for discrete optimum design of truss structures. IMCTS with multiple root nodes includes update process, the best reward, accelerating technique, and terminal condition. 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 multi-objective structural optimization and large-scale structures. |
| title | Improved Monte Carlo tree search formulation with multiple root nodes for discrete sizing optimization of truss structures |
| topic | Artificial Intelligence Numerical Analysis |
| url | https://arxiv.org/abs/2309.06045 |