{"type":"ScholarlyArticle","title":"Automatic Truss Design with Reinforcement Learning","authors":[{"name":"Weihua Du","slug":"weihua-du","given":"Weihua","family":"Du","affiliation":"Institute for Interdisciplinary Information Sciences , Tsinghua University"},{"name":"Jinglun Zhao","slug":"jinglun-zhao","given":"Jinglun","family":"Zhao","affiliation":"Institute for Interdisciplinary Information Sciences , Tsinghua University; Shanghai Qi Zhi Institute"},{"name":"Chao Yu","slug":"chao-yu","given":"Chao","family":"Yu","affiliation":"Institute for Interdisciplinary Information Sciences , Tsinghua University"},{"name":"Xingcheng Yao","slug":"xingcheng-yao","given":"Xingcheng","family":"Yao","affiliation":"Institute for Interdisciplinary Information Sciences , Tsinghua University"},{"name":"Zimeng Song","slug":"zimeng-song","given":"Zimeng","family":"Song","affiliation":"Institute for Interdisciplinary Information Sciences , Tsinghua University"},{"name":"Siyang Wu","slug":"siyang-wu","given":"Siyang","family":"Wu","affiliation":"Institute for Interdisciplinary Information Sciences , Tsinghua University"},{"name":"Ruifeng Luo","slug":"ruifeng-luo","given":"Ruifeng","family":"Luo","affiliation":"East China Architectural Design & Research Institute Co. ,"},{"name":"Zhiyuan Liu","slug":"zhiyuan-liu","given":"Zhiyuan","family":"Liu","affiliation":"Tongji University"},{"name":"Xianzhong Zhao","slug":"xianzhong-zhao","given":"Xianzhong","family":"Zhao","affiliation":"Tongji University; Shanghai Qi Zhi Institute"},{"name":"Yi Wu","slug":"yi-wu","given":"Yi","family":"Wu","affiliation":"Institute for Interdisciplinary Information Sciences , Tsinghua University; Shanghai Qi Zhi Institute; Ltd."}],"abstract":"Truss layout design, namely finding a lightweight truss layout satisfying all the physical constraints, is a fundamental problem in the building industry. Generating the optimal layout is a challenging combinatorial optimization problem, which can be extremely expensive to solve by exhaustive search. Directly applying end-to-end reinforcement learning (RL) methods to truss layout design is infeasible either, since only a tiny portion of the entire layout space is valid under the physical constraints, leading to particularly sparse rewards for RL training.\r\nIn this paper, we develop AutoTruss, a two-stage framework to efficiently generate both lightweight and valid truss layouts. AutoTruss first adopts Monte Carlo tree search to discover a diverse collection of valid layouts. Then RL is applied to iteratively refine the valid solutions. We conduct experiments and ablation studies in popular truss layout design test cases in both 2D and 3D settings.  AutoTruss outperforms the best-reported layouts by 25.1% in the most challenging 3D test cases, resulting in the first effective deep-RL-based approach in the truss layout design literature.","doi":"10.24963/ijcai.2023/407","url":"https://ijcai.org/proceedings/2023/407","pdf":"https://ijcai.org/proceedings/2023/0407.pdf","markdown":"https://ijcai.org/proceedings/2023/407.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/407","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":407,"pages":{"first":3659,"last":3667},"keywords":["Search: S: Combinatorial search and optimisation","Machine Learning: ML: Reinforcement learning","Machine Learning: ML: Applications"],"videos":[],"references":[{"position":1,"raw":"[Alhaddad et al., 2020] Wael Alhaddad, Yahia Halabi, Hu Xu, and HongGang Lei. Outrigger and belt-truss system design for high-rise buildings: A comprehensive review part ii-guideline for optimum topology and size design. Advances in Civil Engineering, 2020, 2020.","title":"Outrigger and belt-truss system design for high-rise buildings: A comprehensive review part ii-guideline for optimum topology and size design","authors":"Wael Alhaddad, Yahia Halabi, Hu Xu, Honggang Lei","year":"2020","venue":"Advances in Civil Engineering","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Assimi et al., 2017] Hirad Assimi, Ali Jamali, and Nader Nariman-Zadeh. Sizing and topology optimization of truss structures using genetic programming. Swarm and evolu- tionary computation, 37:90-103, 2017.","title":"Sizing and topology optimization of truss structures using genetic programming","authors":"Assimi","year":"2017","venue":"Swarm and evolutionary computation","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Banh and Lee, 2019] Thanh T Banh and Dongkyu Lee. Topology optimization of multi-directional variable thick- ness thin plate with multiple materials. Structural and Multidisciplinary Optimization, 59(5):1503-1520, 2019.","title":"Topology optimization of multi-directional variable thickness thin plate with multiple materials","authors":"T Thanh, Dongkyu Banh, Lee","year":"2019","venue":"Structural and Multidisciplinary Optimization","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Banh et al., 2021] Thanh T Banh, Nam G Luu, and Dongkyu Lee. A non-homogeneous multi-material topol- ogy optimization approach for functionally graded struc- tures with cracks. Composite Structures, 273:114230, 2021.","title":"A non-homogeneous multi-material topology optimization approach for functionally graded structures with cracks","authors":"Thanh T Banh, Nam G Luu, Dongkyu Lee","year":"2021","venue":"Composite Structures","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Bello et al., 2016] Irwan Bello, Hieu Pham, Quoc V Le, Mohammad Norouzi, and Samy Bengio. Neural combi- natorial optimization with reinforcement learning. arXiv preprint arXiv:1611.09940, 2016.","title":"Neural combinatorial optimization with reinforcement learning","authors":"Irwan Bello, Hieu Pham, V Quoc, Mohammad Le, Samy Norouzi, Bengio","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Coulom, 2007] Rémi Coulom. Efficient selectivity and backup operators in monte-carlo tree search. In Interna- tional conference on computers and games, pages 72-83. Springer, 2007.","title":"Rémi Coulom. Efficient selectivity and backup operators in monte-carlo tree search","authors":"Coulom","year":"2007","venue":"International conference on computers and games","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Deb and Gulati, 2001] Kalyanmoy Deb and Surendra Gu- lati. Design of truss-structures for minimum weight using genetic algorithms. Finite elements in analysis and design, 37(5):447-465, 2001.","title":"Design of truss-structures for minimum weight using genetic algorithms","authors":"Deb, Gulati ;, Kalyanmoy Deb, Surendra Gulati","year":"2001","venue":"Finite elements in analysis and design","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Deudon et al., 2018] Michel Deudon, Pierre Cournut, Alexandre Lacoste, Yossiri Adulyasak, and Louis-Martin Rousseau. Learning heuristics for the tsp by policy gradient. In International conference on the integration of constraint programming, artificial intelligence, and operations research, pages 170-181. Springer, 2018.","title":"Learning heuristics for the tsp by policy gradient","authors":"Pierre Michel Deudon, Alexandre Cournut, Yossiri Lacoste, Louis-Martin Adulyasak, Rousseau","year":"2018","venue":"International conference on the integration of constraint programming, artificial intelligence, and operations research","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Dorn, 1964] W Dorn. Automatic design of optimal struc- tures. J. de Mecanique, 3:25-52, 1964.","title":"Automatic design of optimal structures","authors":"Dorn","year":"1964","venue":"J. de Mecanique","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Du et al., 2018] Tao Du, Jeevana Priya Inala, Yewen Pu, Andrew Spielberg, Adriana Schulz, Daniela Rus, Ar- mando Solar-Lezama, and Wojciech Matusik. Inversecsg: Automatic conversion of 3d models to csg trees. ACM Transactions on Graphics (TOG), 37(6):1-16, 2018.","title":"Inversecsg: Automatic conversion of 3d models to csg trees","authors":"Tao Du, Jeevana Priya Inala, Yewen Pu, Andrew Spielberg, Adriana Schulz, Daniela Rus, Armando Solar-Lezama, Wojciech Matusik","year":"2018","venue":"ACM Transactions on Graphics (TOG)","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Fenton et al., 2015] Michael Fenton, Ciaran McNally, Jonathan Byrne, Erik Hemberg, James McDermott, and Michael O'Neill. Discrete planar truss optimization by node position variation using grammatical evolu- tion. IEEE Transactions on Evolutionary Computation, 20(4):577-589, 2015.","title":"Discrete planar truss optimization by node position variation using grammatical evolution","authors":"Michael Fenton, Ciaran Mcnally, Jonathan Byrne, Erik Hemberg, James Mcdermott, O' Michael, Neill","year":"2015","venue":"IEEE Transactions on Evolutionary Computation","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Fey and Lenssen, 2019] Matthias Fey and Jan E. Lenssen. Fast graph representation learning with PyTorch Geomet- ric. In ICLR Workshop on Representation Learning on Graphs and Manifolds, 2019.","title":"Fast graph representation learning with PyTorch Geometric","authors":"Fey, Lenssen, Matthias Fey, Jan E Lenssen","year":"2019","venue":"ICLR Workshop on Representation Learning on Graphs and Manifolds","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Fung et al., 2021] Victor Fung, Jiaxin Zhang, Eric Juarez, and Bobby G Sumpter. Benchmarking graph neural net- works for materials chemistry. npj Computational Materi- als, 7(1):1-8, 2021.","title":"Benchmarking graph neural networks for materials chemistry","authors":"Victor Fung, Jiaxin Zhang, Eric Juarez, Bobby G Sumpter","year":"2021","venue":"npj Computational Materials","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Gelly and Silver, 2007] Sylvain Gelly and David Silver. Combining online and offline knowledge in uct. In ICML, 2007.","title":"Combining online and offline knowledge in uct","authors":"Silver Gelly, Sylvain Gelly, David Silver","year":"2007","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Guan et al., 2022] Jiayi Guan, Guang Chen, Jin Huang, Zhijun Li, Lu Xiong, Jing Hou, and Alois Knoll. A dis- crete soft actor-critic decision-making strategy with sam- ple filter for freeway autonomous driving. IEEE Transac- tions on Vehicular Technology, 2022.","title":"A discrete soft actor-critic decision-making strategy with sample filter for freeway autonomous driving","authors":"Jiayi Guan, Guang Chen, Jin Huang, Zhijun Li, Lu Xiong, Jing Hou, Alois Knoll","year":"2022","venue":"IEEE Transactions on Vehicular Technology","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Guo and Wang, 2020] Zhiwei Guo and Heng Wang. A deep graph neural network-based mechanism for social recom- mendations. IEEE Transactions on Industrial Informatics, 17(4):2776-2783, 2020.","title":"A deep graph neural network-based mechanism for social recommendations","authors":"Zhiwei Guo, Heng Wang","year":"2020","venue":"IEEE Transactions on Industrial Informatics","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Haarnoja et al., 2018] Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In ICML, 2018.","title":"Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor","authors":"Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine","year":"2018","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Ho-Huu et al., 2016] V Ho-Huu, T Nguyen-Thoi, T Vo- Duy, and T Nguyen-Trang. An adaptive elitist differen- tial evolution for optimization of truss structures with dis- crete design variables. Computers & Structures, 165:59- 75, 2016.","title":"An adaptive elitist differential evolution for optimization of truss structures with discrete design variables","authors":"T Ho-Huu, T Nguyen-Thoi, T Vo-Duy, Nguyen-Trang","year":"2016","venue":"Computers & Structures","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Jeon and Kim, 2020] Woosung Jeon and Dongsup Kim. Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors. Scientific reports, 10(1):1-11, 2020.","title":"Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors","authors":"Jeon, Kim ;, Woosung Jeon, Dongsup Kim","year":"2020","venue":"Scientific reports","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Khalil et al., 2017] Elias Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, and Le Song. Learning combinatorial opti- mization algorithms over graphs. NeurIPS, 2017.","title":"Learning combinatorial optimization algorithms over graphs","authors":"Elias Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, Le Song","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Klemmt, 2023] Christoph Klemmt. Growth-based method- ology for the topology optimisation of trusses. In Design Modelling Symposium Berlin, pages 467-475. Springer, 2023.","title":"Growth-based methodology for the topology optimisation of trusses","authors":"Christoph Klemmt","year":"2023","venue":"Design Modelling Symposium Berlin","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Kocsis and Szepesvári, 2006] Levente Kocsis and Csaba Szepesvári. Bandit based monte-carlo planning. In Eu- ropean conference on machine learning, pages 282-293. Springer, 2006.","title":"Bandit based monte-carlo planning","authors":"Szepesvári Kocsis, Levente Kocsis, Csaba Szepesvári","year":"2006","venue":"European conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Kool et al., 2018] Wouter Kool, Herke Van Hoof, and Max Welling. Attention, learn to solve routing problems! arXiv preprint arXiv:1803.08475, 2018.","title":"Attention, learn to solve routing problems!","authors":"Kool","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Lamberti, 2008] L Lamberti. An efficient simulated anneal- ing algorithm for design optimization of truss structures. Computers & Structures, 86(19-20):1936-1953, 2008.","title":"An efficient simulated annealing algorithm for design optimization of truss structures","authors":"Lamberti","year":"2008","venue":"Computers & Structures","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Li et al., 2021] Yangyang Li, Yipeng Ji, Shaoning Li, Shu- long He, Yinhao Cao, Yifeng Liu, Hong Liu, Xiong Li, Jun Shi, and Yangchao Yang. Relevance-aware anoma- lous users detection in social network via graph neural net- work. In 2021 International Joint Conference on Neural Networks (IJCNN), pages 1-8. IEEE, 2021.","title":"Relevance-aware anomalous users detection in social network via graph neural net-work","authors":"Yangyang Li, Yipeng Ji, Shaoning Li, Shulong He, Yinhao Cao, Yifeng Liu, Hong Liu, Xiong Li, Jun Shi, Yangchao Yang","year":"2021","venue":"International Joint Conference on Neural Networks (IJCNN)","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Li et al., 2022] Yifei Li, Tao Du, Sangeetha Grama Srini- vasan, Kui Wu, Bo Zhu, Eftychios Sifakis, and Woj- ciech Matusik. Fluidic topology optimization with an anisotropic mixture model. ACM Transactions on Graph- ics (TOG), 41(6):1-14, 2022.","title":"Fluidic topology optimization with an anisotropic mixture model","authors":"Yifei Li, Tao Du, Grama Sangeetha, Kui Srinivasan, Bo Wu, Eftychios Zhu, Wojciech Sifakis, Matusik","year":"2022","venue":"ACM Transactions on Graphics (TOG)","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Lieu, 2022] Qui X Lieu. A novel topology framework for simultaneous topology, size and shape optimization of trusses under static, free vibration and transient behavior. Engineering with Computers, pages 1-25, 2022.","title":"A novel topology framework for simultaneous topology, size and shape optimization of trusses under static, free vibration and transient behavior","authors":"X Qui, Lieu","year":"2022","venue":"Engineering with Computers","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Luh and Lin, 2011] Guan-Chun Luh and Chun-Yi Lin. Op- timal design of truss-structures using particle swarm opti- mization. Computers & structures, 89(23-24):2221-2232, 2011.","title":"Optimal design of truss-structures using particle swarm optimization","authors":"Guan-Chun Luh, Chun-Yi Lin","year":"2011","venue":"Computers & structures","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Luo et al., 2022a] Ruifeng Luo, Yifan Wang, Zhiyuan Liu, Weifang Xiao, and Xianzhong Zhao. A reinforcement learning method for layout design of planar and spa- tial trusses using kernel regression. Applied Sciences, 12(16):8227, 2022.","title":"A reinforcement learning method for layout design of planar and spatial trusses using kernel regression","authors":"Luo","year":"2022","venue":"Applied Sciences","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Luo et al., 2022b] Ruifeng Luo, Yifan Wang, Weifang Xiao, and Xianzhong Zhao. Alphatruss: Monte carlo tree search for optimal truss layout design. Buildings, 12(5):641, 2022.","title":"Alphatruss: Monte carlo tree search for optimal truss layout design","authors":"Luo","year":"2022","venue":"Buildings","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Mai et al., 2021] Hau T Mai, Joowon Kang, and Jaehong Lee. A machine learning-based surrogate model for op- timization of truss structures with geometrically nonlin- ear behavior. Finite Elements in Analysis and Design, 196:103572, 2021.","title":"A machine learning-based surrogate model for optimization of truss structures with geometrically nonlinear behavior","authors":"Joowon Hau T Mai, Jaehong Kang, Lee","year":"2021","venue":"Finite Elements in Analysis and Design","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Mazyavkina et al., 2021] Nina Mazyavkina, Sergey Sviri- dov, Sergei Ivanov, and Evgeny Burnaev. Reinforcement learning for combinatorial optimization: A survey. Com- puters & Operations Research, 134:105400, 2021.","title":"Reinforcement learning for combinatorial optimization: A survey","authors":"Nina Mazyavkina, Sergey Sviridov, Sergei Ivanov, Evgeny Burnaev","year":"2021","venue":"Computers & Operations Research","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Nadaraya, 1964] Elizbar A Nadaraya. On estimating regres- sion. Theory of Probability & Its Applications, 9(1):141- 142, 1964.","title":"On estimating regression","authors":"A Elizbar, Nadaraya","year":"1964","venue":"Theory of Probability & Its Applications","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Nguyen and Banh, 2018] Anh P Nguyen and Thanh T Banh. Design of multiphase carbon fiber reinforcement of crack existing concrete structures using topology op- timization. Steel and Composite Structures, An Interna- tional Journal, 29(5):635-645, 2018.","title":"Design of multiphase carbon fiber reinforcement of crack existing concrete structures using topology optimization. Steel and Composite Structures","authors":"P Anh, Thanh T Nguyen, Banh","year":"2018","venue":"An International Journal","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Permyakov et al., 2006] VO Permyakov, VV Yurchenko, and ID Peleshko. An optimum structural computer-aided design using hybrid genetic algorithm. In Proceeding of the International Conference \"Progress in Steel, Compos- ite and Aluminium Structures\".-Taylor & Francis Group, London, pages 819-826, 2006.","title":"VO Permyakov, VV Yurchenko, and ID Peleshko. An optimum structural computer-aided design using hybrid genetic algorithm","authors":"Permyakov","year":"2006","venue":"Proceeding of the International Conference \"Progress in Steel, Composite and Aluminium Structures","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Scarselli et al., 2008] Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. The graph neural network model. IEEE transactions on neural networks, 20(1):61-80, 2008.","title":"The graph neural network model","authors":"Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, Gabriele Monfardini","year":"2008","venue":"IEEE transactions on neural networks","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Shea and Zhao, 2004] K. Shea and X. Zhao. A novel noon mark cantilever support: from design generation to real- ization. In IASS 2004: Shell and Spatial Sturctures from Models to Realization, 2004.","title":"A novel noon mark cantilever support: from design generation to realization","authors":"K Shea, X Zhao","year":"2004","venue":"IASS 2004: Shell and Spatial Sturctures from Models to Realization","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Silver et al., 2016] David Silver, Aja Huang, Chris J Maddi- son, Arthur Guez, Laurent Sifre, George Van Den Driess- che, Julian Schrittwieser, Ioannis Antonoglou, Veda Pan- neershelvam, Marc Lanctot, et al. Mastering the game of go with deep neural networks and tree search. nature, 529(7587):484-489, 2016.","title":"Mastering the game of go with deep neural networks and tree search","authors":"David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den, Marc Driessche, Lanctot","year":"2016","venue":"nature","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Stolpe, 2016] Mathias Stolpe. Truss optimization with dis- crete design variables: a critical review. Structural and Multidisciplinary Optimization, 53(2):349-374, 2016.","title":"Truss optimization with discrete design variables: a critical review","authors":"Mathias Stolpe","year":"2016","venue":"Structural and Multidisciplinary Optimization","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Taylor et al., 2021] Annalisa T Taylor, Thomas A Berrueta, and T D Murphey. Active learning in robotics: A review of control principles. Mechatronics, 77:102576, 2021.","title":"Active learning in robotics: A review of control principles","authors":"Annalisa T Taylor, Thomas A Berrueta, T D Murphey","year":"2021","venue":"Mechatronics","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Tejani et al., 2018] Ghanshyam G Tejani, Vimal J Savsani, Vivek K Patel, and Poonam V Savsani. Size, shape, and topology optimization of planar and space trusses using mutation-based improved metaheuristics. Journal of Com- putational Design and Engineering, 5(2):198-214, 2018.","title":"Size, shape, and topology optimization of planar and space trusses using mutation-based improved metaheuristics","authors":"G Ghanshyam, Tejani, J Vimal, Savsani, K Vivek, Poonam V Patel, Savsani","year":"2018","venue":"Journal of Computational Design and Engineering","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. NeurIPS, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, Illia Polosukhin","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Wang et al., 2019] Zhenwei Wang, Congcong Luan, Guangxin Liao, Xinhua Yao, and Jianzhong Fu. Me- chanical and self-monitoring behaviors of 3d printing smart continuous carbon fiber-thermoplastic lattice truss sandwich structure. Composites Part B: Engineering, 176:107215, 2019.","title":"Mechanical and self-monitoring behaviors of 3d printing smart continuous carbon fiber-thermoplastic lattice truss sandwich structure","authors":"Zhenwei Wang, Congcong Luan, Guangxin Liao, Xinhua Yao, Jianzhong Fu","year":"2019","venue":"Composites Part B: Engineering","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Wu et al., 2019a] Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and T Tan. Session-based rec- ommendation with graph neural networks. In AAAI, 2019.","title":"Session-based recommendation with graph neural networks","authors":"Wu","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Wu et al., 2019b] Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, and Andrew Lim. Learning improvement heuristics for solving the travelling salesman problem. arXiv preprint arXiv:1912.05784, 2019.","title":"Learning improvement heuristics for solving the travelling salesman problem","authors":"Wu","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Xie and Grossman, 2018] Tian Xie and Jeffrey C Gross- man. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material proper- ties. Physical review letters, 120(14):145301, 2018.","title":"Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties","authors":"Tian Xie, Jeffrey C Grossman","year":"2018","venue":"Physical review letters","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Yang et al., 2021] Qiong Yang, Hongchao Ji, Hongmei Lu, and Zhimin Zhang. Prediction of liquid chromato- graphic retention time with graph neural networks to as- sist in small molecule identification. Analytical Chemistry, 93(4):2200-2206, 2021.","title":"Prediction of liquid chromatographic retention time with graph neural networks to assist in small molecule identification","authors":"Qiong Yang, Hongchao Ji, Hongmei Lu, Zhimin Zhang","year":"2021","venue":"Analytical Chemistry","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Yee et al., 2016] Timothy Yee, Viliam Lisỳ, Michael H Bowling, and S Kambhampati. Monte carlo tree search in continuous action spaces with execution uncertainty. In IJCAI, pages 690-697, 2016.","title":"Monte carlo tree search in continuous action spaces with execution uncertainty","authors":"Timothy Yee, Viliam Lisỳ, Michael H Bowling, Kambhampati","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Yoshimori et al., 2020] Atsushi Yoshimori, Enzo Kawasaki, Chisato Kanai, and Tomohiko Tasaka. Strategies for de- sign of molecular structures with a desired pharmacophore using deep reinforcement learning. Chemical and Phar- maceutical Bulletin, 68(3):227-233, 2020.","title":"Strategies for design of molecular structures with a desired pharmacophore using deep reinforcement learning","authors":"Atsushi Yoshimori, Enzo Kawasaki, Chisato Kanai, Tomohiko Tasaka","year":"2020","venue":"Chemical and Pharmaceutical Bulletin","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Zhou et al., 2022] Haibin Zhou, Zichuan Lin, Junyou Li, Deheng Ye, Qiang Fu, and Wei Yang. Revisiting discrete soft actor-critic. arXiv preprint arXiv:2209.10081, 2022.","title":"Revisiting discrete soft actor-critic","authors":"Haibin Zhou, Zichuan Lin, Junyou Li, Deheng Ye, Qiang Fu, Wei Yang","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}