{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T01:52:51Z","timestamp":1779241971575,"version":"3.51.4"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T00:00:00Z","timestamp":1690588800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T00:00:00Z","timestamp":1690588800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Equipment advance research project","award":["50912020401"],"award-info":[{"award-number":["50912020401"]}]},{"name":"Equipment advance research project","award":["50912020401"],"award-info":[{"award-number":["50912020401"]}]},{"name":"Equipment advance research project","award":["50912020401"],"award-info":[{"award-number":["50912020401"]}]},{"name":"Equipment advance research project","award":["50912020401"],"award-info":[{"award-number":["50912020401"]}]},{"name":"Hunan Key Laboratory of intelligent decision-making technology for emergency management","award":["2020TP1013"],"award-info":[{"award-number":["2020TP1013"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s11227-023-05551-2","type":"journal-article","created":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T09:01:39Z","timestamp":1690621299000},"page":"2319-2346","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Evolutionary reinforcement learning algorithm for large-scale multi-agent cooperation and confrontation applications"],"prefix":"10.1007","volume":"80","author":[{"given":"Haiying","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"ZhiHao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuihua","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangquan","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tiexiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,29]]},"reference":[{"key":"5551_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-021-04094-8","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Zhao H (2022) A multi-agent model for decision making on environmental regulation in urban agglomeration[J]. J Supercomput. https:\/\/doi.org\/10.1007\/s11227-021-04094-8","journal-title":"J Supercomput"},{"key":"5551_CR2","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1007\/s13177-017-0142-6","volume":"16","author":"H Hamidi","year":"2018","unstructured":"Hamidi H, Kamankesh A (2018) An approach to intelligent traffic management system using a multi-agent system[J]. Int J Intell Transp Syst Res 16:112\u2013124. https:\/\/doi.org\/10.1007\/s13177-017-0142-6","journal-title":"Int J Intell Transp Syst Res"},{"key":"5551_CR3","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/s11227-020-03264-4","volume":"77","author":"F Ye","year":"2021","unstructured":"Ye F, Chen J, Sun Q et al (2021) Decentralized task allocation for heterogeneous multi-UAV system with task coupling constraints[J]. J Supercomput 77:111\u2013132. https:\/\/doi.org\/10.1007\/s11227-020-03264-4","journal-title":"J Supercomput"},{"key":"5551_CR4","doi-asserted-by":"publisher","first-page":"101964","DOI":"10.1016\/j.artmed.2020.101964","volume":"109","author":"A Coronato","year":"2020","unstructured":"Coronato A, Naeem M, Pietro GD et al (2020) Reinforcement learning for intelligent healthcare applications: a survey[J]. Artif Intell Med 109:101964. https:\/\/doi.org\/10.1016\/j.artmed.2020.101964","journal-title":"Artif Intell Med"},{"issue":"6","key":"5551_CR5","doi-asserted-by":"publisher","first-page":"4909","DOI":"10.1109\/TITS.2021.3054625","volume":"23","author":"BR Kiran","year":"2021","unstructured":"Kiran BR, Sobh I, Talpaert V et al (2021) Deep reinforcement learning for autonomous driving: a survey[J]. IEEE Trans Intell Transp Syst 23(6):4909\u20134926. https:\/\/doi.org\/10.1109\/TITS.2021.3054625","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"5551_CR6","doi-asserted-by":"publisher","unstructured":"Johannink T, Bahl S, Nair A, et al (2019) Residual reinforcement learning for robot control[C]. In: 2019 International Conference on Robotics and Automation (ICRA), IEEE, pp 6023\u20136029. https:\/\/doi.org\/10.1109\/ICRA.2019.8794127","DOI":"10.1109\/ICRA.2019.8794127"},{"key":"5551_CR7","first-page":"6382","volume":"30","author":"R Lowe","year":"2017","unstructured":"Lowe R, Wu YI, Tamar A et al (2017) Multi-agent actor-critic for mixed cooperative-competitive environments. Adv Neural Inf Process Syst 30:6382\u20136393","journal-title":"Adv Neural Inf Process Syst"},{"key":"5551_CR8","doi-asserted-by":"publisher","unstructured":"Foerster J, Farquhar G, Afouras T, et al. (2018) Counterfactual multi-agent policy gradients[C]. In: Proceedings of the AAAI Conference on Artificial Intelligence. https:\/\/doi.org\/10.1609\/aaai.v32i1.11794","DOI":"10.1609\/aaai.v32i1.11794"},{"issue":"2","key":"5551_CR9","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1109\/LRA.2019.2891991","volume":"4","author":"F Niroui","year":"2019","unstructured":"Niroui F, Zhang K, Kashino Z et al (2019) Deep reinforcement learning robot for search and rescue applications: exploration in unknown cluttered environments[J]. IEEE Robot Autom Lett 4(2):610\u2013617. https:\/\/doi.org\/10.1109\/LRA.2019.2891991","journal-title":"IEEE Robot Autom Lett"},{"key":"5551_CR10","unstructured":"Yang Y, Luo R, Li M, et al (2018) Mean field multi-agent reinforcement learning[C]. In: International Conference on Machine Learning. PMLR, pp 5571\u20135580"},{"key":"5551_CR11","unstructured":"Iqbal S, Sha F (2019) Actor-attention-critic for multi-agent reinforcement learning[C]. In: International Conference on Machine Learning. PMLR, pp 2961\u20132970"},{"key":"5551_CR12","unstructured":"Christianos F, Papoudakis G, Rahman MA, et al (2021) Scaling multi-agent reinforcement learning with selective parameter sharing[C]. In: International Conference on Machine Learning. PMLR, pp 1989\u20131998"},{"key":"5551_CR13","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.swevo.2018.03.011","volume":"44","author":"MM Drugan","year":"2019","unstructured":"Drugan MM (2019) Reinforcement learning versus evolutionary computation: a survey on hybrid algorithms[J]. Swarm Evol Comput 44:228\u2013246. https:\/\/doi.org\/10.1016\/j.swevo.2018.03.011","journal-title":"Swarm Evol Comput"},{"key":"5551_CR14","doi-asserted-by":"publisher","unstructured":"Bodnar C, Day B, Li\u00f3 P (2020) Proximal distilled evolutionary reinforcement learning[C]. In: Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), pp 3283\u20133290. https:\/\/doi.org\/10.1609\/aaai.v34i04.5728","DOI":"10.1609\/aaai.v34i04.5728"},{"key":"5551_CR15","unstructured":"Majumdar S, Khadka S, Miret S, et al (2020) Evolutionary reinforcement learning for sample-efficient multiagent coordination[C]. In: International Conference on Machine Learning. PMLR, pp 6651\u20136660"},{"key":"5551_CR16","first-page":"1196","volume":"31","author":"S Khadka","year":"2018","unstructured":"Khadka S, Tumer K (2018) Evolution-guided policy gradient in reinforcement learning. Adv Neural Inf Process Syst 31:1196\u20131208","journal-title":"Adv Neural Inf Process Syst"},{"key":"5551_CR17","first-page":"5032","volume":"31","author":"E Conti","year":"2018","unstructured":"Conti E, Madhavan V, Petroski SF et al (2018) Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents. Adv Neural Inf Process Syst 31:5032\u20135043","journal-title":"Adv Neural Inf Process Syst"},{"key":"5551_CR18","first-page":"56","volume":"2018","author":"V Shopov","year":"2018","unstructured":"Shopov V, Markova V (2018) A study of the impact of evolutionary strategies on performance of reinforcement learning autonomous agents[J]. ICAS 2018:56\u201360","journal-title":"ICAS"},{"key":"5551_CR19","unstructured":"Czarnecki W, Jayakumar S, Jaderberg M, et al (2018) Mix and match agent curricula for reinforcement learning[C]. In: International Conference on Machine Learning. PMLR, pp 1087\u20131095s"},{"key":"5551_CR20","doi-asserted-by":"crossref","unstructured":"Li Z, Liu H, Huang K, Cheng G, Wang R (2022) Multi-domain cooperative action planning strategy based on reinforcement learning. In: 2022 IEEE International Conference on Unmanned Systems (ICUS), Guangzhou, China, pp 910\u2013915","DOI":"10.1109\/ICUS55513.2022.9987011"},{"key":"5551_CR21","first-page":"157","volume":"1994","author":"ML Littman","year":"1994","unstructured":"Littman ML (1994) Markov games as a framework for multi-agent reinforcement learning[M]. Machine learning proceedings. Morgan Kaufmann 1994:157\u2013163","journal-title":"Morgan Kaufmann"},{"key":"5551_CR22","unstructured":"Mao H, Zhang Z, Xiao Z et al. (2018) Modelling the dynamic joint policy of teammates with rattention multi-agent DDPG[C]. In: Adaptive Agents and Multi-Agents Systems.International Foundation for Autonomous Agents and Multiagent Systems, ACM, pp 1108\u20131116"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05551-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-023-05551-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05551-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,15]],"date-time":"2024-01-15T09:28:24Z","timestamp":1705310904000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-023-05551-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,29]]},"references-count":22,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["5551"],"URL":"https:\/\/doi.org\/10.1007\/s11227-023-05551-2","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,29]]},"assertion":[{"value":"14 July 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2023","order":2,"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"}}]}}