{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T04:01:42Z","timestamp":1779940902731,"version":"3.53.1"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T00:00:00Z","timestamp":1731715200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T00:00:00Z","timestamp":1731715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Evol. Intel."],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>The job shop scheduling problem (JSSP) is a well-known NP-hard combinatorial optimization problem that focuses on assigning tasks to limited resources while adhering to certain constraints. Currently, deep reinforcement learning (DRL)-based solutions are being widely used to solve the JSSP by defining the problem structure on disjunctive graphs. Some of the proposed approaches attempt to leverage the structural information of the JSSP to capture the dynamics of the environment without considering the time dependency within the JSSP. However, learning graph representations only from the structural relationship of nodes results in a weak and incomplete representation of these graphs which does not provide an expressive representation of the dynamics in the environment. In this study, unlike existing frameworks, we defined the JSSP as a dynamic graph to explicitly consider the time-varying aspect of the JSSP environment. To this end, we propose a novel DRL framework that captures both the spatial and temporal attributes of the JSSP to construct rich and complete graph representations. Our DRL framework introduces a novel attentive graph isomorphism network (Attentive-GIN)-based spatial block to learn the structural relationship and a temporal block to capture the time dependency. Additionally, we designed a gated fusion block that selectively combines the learned representations from the two blocks. We trained the model using the proximal policy optimization algorithm of reinforcement learning. Experimental results show that our trained model exhibits significant performance enhancement compared to heuristic dispatching rules and learning-based solutions for both randomly generated datasets and public benchmarks.<\/jats:p>","DOI":"10.1007\/s12065-024-00989-6","type":"journal-article","created":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T01:23:20Z","timestamp":1731720200000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Deep reinforcement learning-based spatio-temporal graph neural network for solving job shop scheduling problem"],"prefix":"10.1007","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7836-8399","authenticated-orcid":false,"given":"Goytom","family":"Gebreyesus","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Getu","family":"Fellek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Farid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sicheng","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shigeru","family":"Fujimura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Osamu","family":"Yoshie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,16]]},"reference":[{"key":"989_CR1","doi-asserted-by":"publisher","first-page":"1367","DOI":"10.1007\/s10845-019-01515-7","volume":"31","author":"TT Mezgebe","year":"2020","unstructured":"Mezgebe TT, Bril El Haouzi H, Demesure G, Pannequin R, Thomas A (2020) Multi-agent systems negotiation to deal with dynamic scheduling in disturbed industrial context. J Intell Manuf 31:1367\u20131382","journal-title":"J Intell Manuf"},{"issue":"9","key":"989_CR2","doi-asserted-by":"publisher","first-page":"9535","DOI":"10.1109\/TII.2022.3230785","volume":"19","author":"DM Sime","year":"2022","unstructured":"Sime DM, Wang G, Zeng Z, Wang W, Peng B (2022) Semi-supervised defect segmentation with pairwise similarity map consistency and ensemble-based cross-pseudo labels. IEEE Trans Ind Inform 19(9):9535\u20139545","journal-title":"IEEE Trans Ind Inform"},{"issue":"1","key":"989_CR3","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1007\/s12065-023-00885-5","volume":"17","author":"D Qiao","year":"2023","unstructured":"Qiao D, Duan L, Li H, Xiao Y (2023) Optimization of job shop scheduling problem based on deep reinforcement learning. Evolut Intell 17(1):371\u2013383","journal-title":"Evolut Intell"},{"key":"989_CR4","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1007\/s00170-006-0662-8","volume":"34","author":"Z Zhang","year":"2007","unstructured":"Zhang Z, Zheng L, Weng MX (2007) Dynamic parallel machine scheduling with mean weighted tardiness objective by q-learning. Int J Adv Manuf Technol 34:968\u2013980","journal-title":"Int J Adv Manuf Technol"},{"key":"989_CR5","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.cie.2017.05.026","volume":"110","author":"J Shahrabi","year":"2017","unstructured":"Shahrabi J, Adibi MA, Mahootchi M (2017) A reinforcement learning approach to parameter estimation in dynamic job shop scheduling. Comput Ind Eng 110:75\u201382","journal-title":"Comput Ind Eng"},{"key":"989_CR6","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.procir.2020.05.210","volume":"97","author":"C Kardos","year":"2021","unstructured":"Kardos C, Laflamme C, Gallina V, Sihn W (2021) Dynamic scheduling in a job-shop production system with reinforcement learning. Procedia CIRP 97:104\u2013109","journal-title":"Procedia CIRP"},{"key":"989_CR7","doi-asserted-by":"publisher","first-page":"122995","DOI":"10.1109\/ACCESS.2021.3110242","volume":"9","author":"Y Zhao","year":"2021","unstructured":"Zhao Y, Wang Y, Tan Y, Zhang J, Yu H (2021) Dynamic jobshop scheduling algorithm based on deep q network. IEEE Access 9:122995\u2013123011","journal-title":"IEEE Access"},{"issue":"9","key":"989_CR8","doi-asserted-by":"publisher","first-page":"5177","DOI":"10.3390\/su14095177","volume":"14","author":"M Zhang","year":"2022","unstructured":"Zhang M, Lu Y, Hu Y, Amaitik N, Xu Y (2022) Dynamic scheduling method for job-shop manufacturing systems by deep reinforcement learning with proximal policy optimization. Sustainability 14(9):5177","journal-title":"Sustainability"},{"key":"989_CR9","doi-asserted-by":"publisher","unstructured":"Zhao L, Shen W, Zhang C, Peng K (2022) An end-to-end deep reinforcement learning approach for job shop scheduling. In: 2022 25th international conference on computer supported cooperative work in design (CSCWD), pp. 841-846. IEEE. https:\/\/doi.org\/10.1109\/CSCWD54268.2022.9776116","DOI":"10.1109\/CSCWD54268.2022.9776116"},{"key":"989_CR10","unstructured":"Bello I, Pham H, Le QV, Norouzi M, Bengio S (2016) Neural combinatorial optimization with reinforcement learning. arXiv preprint arXiv:1611.09940"},{"key":"989_CR11","unstructured":"Nazari M, Oroojlooy A, Snyder L, Tak\u00e1c M (2018) Reinforcement learning for solving the vehicle routing problem. Advances in neural information processing systems 31"},{"key":"989_CR12","first-page":"1621","volume":"33","author":"C Zhang","year":"2020","unstructured":"Zhang C, Song W, Cao Z, Zhang J, Tan PS, Chi X (2020) Learning to dispatch for job shop scheduling via deep reinforcement learning. Adv Neural Inform Process Syst 33:1621\u20131632","journal-title":"Adv Neural Inform Process Syst"},{"key":"989_CR13","doi-asserted-by":"crossref","unstructured":"Yang S (2022) Using attention mechanism to solve job shop scheduling problem. In: 2022 2nd international conference on consumer electronics and computer engineering (ICCECE), pp. 59\u201362. IEEE","DOI":"10.1109\/ICCECE54139.2022.9712705"},{"issue":"2","key":"989_CR14","doi-asserted-by":"publisher","first-page":"1322","DOI":"10.1109\/TII.2022.3167380","volume":"19","author":"R Chen","year":"2022","unstructured":"Chen R, Li W, Yang H (2022) A deep reinforcement learning framework based on an attention mechanism and disjunctive graph embedding for the job-shop scheduling problem. IEEE Trans Ind Inform 19(2):1322\u20131331","journal-title":"IEEE Trans Ind Inform"},{"issue":"6","key":"989_CR15","doi-asserted-by":"publisher","first-page":"3337","DOI":"10.1109\/TITS.2020.2983763","volume":"22","author":"M Lv","year":"2020","unstructured":"Lv M, Hong Z, Chen L, Chen T, Zhu T, Ji S (2020) Temporal multi-graph convolutional network for traffic flow prediction. IEEE Trans Intell Transp Syst 22(6):3337\u20133348","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"11","key":"989_CR16","doi-asserted-by":"publisher","first-page":"3360","DOI":"10.1080\/00207543.2020.1870013","volume":"59","author":"J Park","year":"2021","unstructured":"Park J, Chun J, Kim SH, Kim Y, Park J (2021) Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning. Int J Prod Res 59(11):3360\u20133377","journal-title":"Int J Prod Res"},{"issue":"2","key":"989_CR17","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1002\/nav.3800060205","volume":"6","author":"HM Wagner","year":"1959","unstructured":"Wagner HM (1959) An integer linear-programming model for machine scheduling. Naval Res Logist Q 6(2):131\u2013140","journal-title":"Naval Res Logist Q"},{"key":"989_CR18","first-page":"277","volume":"93","author":"E Pinson","year":"1995","unstructured":"Pinson E (1995) The job shop scheduling problem: A concise survey and some recent developments. Sched Theory Appl 93:277\u2013294","journal-title":"Sched Theory Appl"},{"key":"989_CR19","doi-asserted-by":"publisher","first-page":"1581","DOI":"10.1007\/s12065-020-00426-4","volume":"14","author":"B Kurniawan","year":"2021","unstructured":"Kurniawan B, Song W, Weng W, Fujimura S (2021) Distributed-elite local search based on a genetic algorithm for bi-objective job-shop scheduling under time-of-use tariffs. Evolut Intell 14:1581\u20131595","journal-title":"Evolut Intell"},{"key":"989_CR20","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.promfg.2019.02.006","volume":"30","author":"J Mohan","year":"2019","unstructured":"Mohan J, Lanka K, Rao AN (2019) A review of dynamic job shop scheduling techniques. Procedia Manuf 30:34\u201339","journal-title":"Procedia Manuf"},{"key":"989_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122135","volume":"238","author":"S Hou","year":"2024","unstructured":"Hou S, Gebreyesus GD, Fujimura S (2024) Day-ahead multi-modal demand side management in microgrid via two-stage improved ring-topology particle swarm optimization. Expert Syst Appl 238:122135","journal-title":"Expert Syst Appl"},{"issue":"4","key":"989_CR22","doi-asserted-by":"publisher","first-page":"2044","DOI":"10.1109\/TII.2014.2342378","volume":"10","author":"H Gao","year":"2014","unstructured":"Gao H, Kwong S, Fan B, Wang R (2014) A hybrid particle-swarm tabu search algorithm for solving job shop scheduling problems. IEEE Trans Ind Inform 10(4):2044\u20132054","journal-title":"IEEE Trans Ind Inform"},{"key":"989_CR23","doi-asserted-by":"crossref","unstructured":"Yamada T, Nakano R (1997) Genetic algorithms for job-shop scheduling problems. Proceedings of modern heuristic for decision support 6781","DOI":"10.1049\/PBCE055E_ch7"},{"issue":"8","key":"989_CR24","doi-asserted-by":"publisher","first-page":"3710","DOI":"10.3390\/app11083710","volume":"11","author":"B Cunha","year":"2021","unstructured":"Cunha B, Madureira A, Fonseca B, Matos J (2021) Intelligent scheduling with reinforcement learning. Appl Sci 11(8):3710","journal-title":"Appl Sci"},{"issue":"4","key":"989_CR25","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1007\/s12065-019-00278-7","volume":"12","author":"C Sur","year":"2019","unstructured":"Sur C (2019) Ucrlf: unified constrained reinforcement learning framework for phase-aware architectures for autonomous vehicle signaling and trajectory optimization. Evolut Intell 12(4):689\u2013712","journal-title":"Evolut Intell"},{"key":"989_CR26","doi-asserted-by":"publisher","first-page":"4211","DOI":"10.1007\/s10489-019-01487-4","volume":"49","author":"S Ding","year":"2019","unstructured":"Ding S, Du W, Zhao X, Wang L, Jia W (2019) A new asynchronous reinforcement learning algorithm based on improved parallel pso. Appl Intell 49:4211\u20134222","journal-title":"Appl Intell"},{"issue":"1","key":"989_CR27","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1007\/s12065-022-00778-z","volume":"17","author":"L Zhang","year":"2022","unstructured":"Zhang L, Zhang Y, Liu S, Chen L, Liang X, Cheng G, Liu Z (2024) Orad: a new framework of offline reinforcement learning with q-value regularization. Evolut Intell 17(1):339\u2013347","journal-title":"Evolut Intell"},{"key":"989_CR28","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1007\/s10489-018-1296-x","volume":"49","author":"X Zhao","year":"2019","unstructured":"Zhao X, Ding S, An Y, Jia W (2019) Applications of asynchronous deep reinforcement learning based on dynamic updating weights. Appl Intell 49:581\u2013591","journal-title":"Appl Intell"},{"key":"989_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-021-01847-3","author":"B Kayhan","year":"2021","unstructured":"Kayhan B, Yildiz G (2021) Reinforcement learning applications to machine scheduling problems: a comprehensive literature review. J Intell Manuf. https:\/\/doi.org\/10.1007\/s10845-021-01847-3","journal-title":"J Intell Manuf"},{"key":"989_CR30","unstructured":"Tassel P, Gebser M, Schekotihin K (2021) A reinforcement learning environment for job-shop scheduling"},{"key":"989_CR31","doi-asserted-by":"publisher","unstructured":"Hammami NEH, Lardeux B, Hadj-Alouane A, Jridi M (2022) Job shop scheduling: A novel drl approach for continuous schedule-generation facing real-time job arrivals. IFAC-PapersOnLine 55, 2493\u20132498 https:\/\/doi.org\/10.1016\/j.ifacol.2022.10.083","DOI":"10.1016\/j.ifacol.2022.10.083"},{"issue":"2","key":"989_CR32","doi-asserted-by":"publisher","first-page":"375","DOI":"10.2507\/IJSIMM20-2-CO7","volume":"20","author":"B Han","year":"2021","unstructured":"Han B, Yang J (2021) A deep reinforcement learning based solution for flexible job shop scheduling problem. Int J Simul Modell 20(2):375\u2013386","journal-title":"Int J Simul Modell"},{"issue":"5","key":"989_CR33","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1002\/tee.23771","volume":"18","author":"G Fellek","year":"2023","unstructured":"Fellek G, Farid A, Gebreyesus G, Fujimura S, Yoshie O (2023) Graph transformer with reinforcement learning for vehicle routing problem. IEEJ Trans Electr Electron Eng 18(5):701\u2013713","journal-title":"IEEJ Trans Electr Electron Eng"},{"issue":"6","key":"989_CR34","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1002\/tee.23788","volume":"18","author":"G Gebreyesus","year":"2023","unstructured":"Gebreyesus G, Fellek G, Farid A, Fujimura S, Yoshie O (2023) Gated-attention model with reinforcement learning for solving dynamic job shop scheduling problem. IEEJ Trans Electr Electron Eng 18(6):932\u2013944","journal-title":"IEEJ Trans Electr Electron Eng"},{"issue":"2","key":"989_CR35","doi-asserted-by":"publisher","first-page":"1600","DOI":"10.1109\/TII.2022.3189725","volume":"19","author":"W Song","year":"2022","unstructured":"Song W, Chen X, Li Q, Cao Z (2022) Flexible job-shop scheduling via graph neural network and deep reinforcement learning. IEEE Trans Ind Inform 19(2):1600\u20131610","journal-title":"IEEE Trans Ind Inform"},{"key":"989_CR36","doi-asserted-by":"crossref","unstructured":"Gunarathna U, Borovica-Gajic R, Karunasekara S, Tanin E (2022) Solving dynamic graph problems with multi-attention deep reinforcement learning. arXiv preprint arXiv:2201.04895","DOI":"10.1145\/3557915.3560956"},{"key":"989_CR37","doi-asserted-by":"crossref","unstructured":"Wu G, Zhang Z, Liu H, Wang J (2021) Solving time-dependent traveling salesman problem with time windows with deep reinforcement learning. In: 2021 IEEE international conference on systems, man, and cybernetics (SMC), pp 558\u2013563. IEEE","DOI":"10.1109\/SMC52423.2021.9658956"},{"key":"989_CR38","unstructured":"Xu K, Hu W, Leskovec J, Jegelka S (2018) How powerful are graph neural networks? arXiv preprint arXiv:1810.00826"},{"key":"989_CR39","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. arXiv preprint arXiv:1710.10903"},{"key":"989_CR40","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3089179","author":"Y Xu","year":"2021","unstructured":"Xu Y, Fang M, Chen L, Xu G, Du Y, Zhang C (2021) Reinforcement learning with multiple relational attention for solving vehicle routing problems. IEEE Trans Cybern. https:\/\/doi.org\/10.1109\/TCYB.2021.3089179","journal-title":"IEEE Trans Cybern"},{"key":"989_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.tre.2023.103095","volume":"173","author":"F Guo","year":"2023","unstructured":"Guo F, Wei Q, Wang M, Guo Z, Wallace SW (2023) Deep attention models with dimension-reduction and gate mechanisms for solving practical time-dependent vehicle routing problems. Transp Res Part E: Logist Transp Rev 173:103095","journal-title":"Transp Res Part E: Logist Transp Rev"},{"key":"989_CR42","unstructured":"Parisotto E, Song F, Rae J, Pascanu R, Gulcehre C, Jayakumar S, Jaderberg M, Kaufman RL, Clark A, Noury S, et al (2020) Stabilizing transformers for reinforcement learning. In: International conference on machine learning, pp. 7487\u20137498. PMLR"},{"issue":"3","key":"989_CR43","first-page":"5103","volume":"73","author":"EK Elsayed","year":"2022","unstructured":"Elsayed EK, Elsayed AK, Eldahshan KA (2022) Deep reinforcement learning-based job shop scheduling of smart manufacturing. CMC-Comput Mater Contin 73(3):5103\u20135120","journal-title":"CMC-Comput Mater Contin"},{"issue":"15","key":"989_CR44","doi-asserted-by":"publisher","first-page":"4255","DOI":"10.1080\/00207543.2011.611539","volume":"50","author":"V Sels","year":"2012","unstructured":"Sels V, Gheysen N, Vanhoucke M (2012) A comparison of priority rules for the job shop scheduling problem under different flow time-and tardiness-related objective functions. Int J Prod Res 50(15):4255\u20134270","journal-title":"Int J Prod Res"},{"issue":"2","key":"989_CR45","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1016\/0377-2217(93)90182-M","volume":"64","author":"E Taillard","year":"1993","unstructured":"Taillard E (1993) Benchmarks for basic scheduling problems. Eur J Op Res 64(2):278\u2013285","journal-title":"Eur J Op Res"},{"issue":"2","key":"989_CR46","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1016\/S0377-2217(98)00113-1","volume":"113","author":"AS Jain","year":"1999","unstructured":"Jain AS, Meeran S (1999) Deterministic job-shop scheduling: Past, present and future. Eur J Op Res 113(2):390\u2013434","journal-title":"Eur J Op Res"},{"issue":"1","key":"989_CR47","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/S0377-2217(97)00019-2","volume":"109","author":"E Demirkol","year":"1998","unstructured":"Demirkol E, Mehta S, Uzsoy R (1998) Benchmarks for shop scheduling problems. Eur J Op Res 109(1):137\u2013141","journal-title":"Eur J Op Res"},{"key":"989_CR48","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/1090.001.0001","volume-title":"Adaptation in Natural and Artificial Systems: an Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence","author":"JH Holland","year":"1992","unstructured":"Holland JH (1992) Adaptation in Natural and Artificial Systems: an Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT press, Cambridge, MA"},{"key":"989_CR49","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.ins.2012.06.032","volume":"217","author":"E Duman","year":"2012","unstructured":"Duman E, Uysal M, Alkaya AF (2012) Migrating birds optimization: a new metaheuristic approach and its performance on quadratic assignment problem. Inform Sci 217:65\u201377","journal-title":"Inform Sci"},{"key":"989_CR50","doi-asserted-by":"crossref","unstructured":"Bhatt N, Chauhan NR (2015) Genetic algorithm applications on job shop scheduling problem: A review. In: 2015 International conference on soft computing techniques and implementations (ICSCTI), pp 7\u201314. IEEE","DOI":"10.1109\/ICSCTI.2015.7489556"},{"key":"989_CR51","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.swevo.2017.06.003","volume":"38","author":"T Meng","year":"2018","unstructured":"Meng T, Pan Q-K, Li J-Q, Sang H-Y (2018) An improved migrating birds optimization for an integrated lot-streaming flow shop scheduling problem. Sw Evolut Comput 38:64\u201378","journal-title":"Sw Evolut Comput"},{"issue":"2","key":"989_CR52","doi-asserted-by":"publisher","first-page":"44","DOI":"10.3390\/a13020044","volume":"13","author":"H Li","year":"2020","unstructured":"Li H, Zhu H, Jiang T (2020) Modified migrating birds optimization for energy-aware flexible job shop scheduling problem. Algorithms 13(2):44","journal-title":"Algorithms"},{"key":"989_CR53","doi-asserted-by":"publisher","first-page":"186474","DOI":"10.1109\/ACCESS.2020.3029868","volume":"8","author":"B-A Han","year":"2020","unstructured":"Han B-A, Yang J-J (2020) Research on adaptive job shop scheduling problems based on dueling double dqn. IEEE Access 8:186474\u2013186495. https:\/\/doi.org\/10.1109\/ACCESS.2020.3029868","journal-title":"IEEE Access"},{"issue":"7540","key":"989_CR54","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G et al (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529\u2013533","journal-title":"Nature"},{"key":"989_CR55","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141 (2017) Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30"}],"container-title":["Evolutionary Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12065-024-00989-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12065-024-00989-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12065-024-00989-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T06:30:01Z","timestamp":1740119401000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12065-024-00989-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,16]]},"references-count":55,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["989"],"URL":"https:\/\/doi.org\/10.1007\/s12065-024-00989-6","relation":{},"ISSN":["1864-5909","1864-5917"],"issn-type":[{"value":"1864-5909","type":"print"},{"value":"1864-5917","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,16]]},"assertion":[{"value":"1 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 September 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 November 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"6"}}