{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T16:07:13Z","timestamp":1790698033857,"version":"4.1.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T00:00:00Z","timestamp":1786233600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T00:00:00Z","timestamp":1786233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Innovation Capability Improvement Plan Project of Hebei Province","award":["22567626H"],"award-info":[{"award-number":["22567626H"]}]},{"name":"Supported by Hebei Natural Science Foundation","award":["F2022203015"],"award-info":[{"award-number":["F2022203015"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Vis"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1007\/s12650-026-01151-3","type":"journal-article","created":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T09:29:29Z","timestamp":1786267769000},"page":"1049-1065","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["3DMAViz: a visual analytics framework for exploring DRL-based 3D multi-agent environments"],"prefix":"10.1007","volume":"29","author":[{"given":"Shida","family":"Gao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisha","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liwen","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongliang","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,9]]},"reference":[{"key":"1151_CR1","unstructured":"Abadi M, Agarwal A, Barham P et al (2016) Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467"},{"issue":"8","key":"1151_CR2","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.3390\/electronics9081295","volume":"9","author":"M Ahmed","year":"2020","unstructured":"Ahmed M, Seraj R, Islam SMS (2020) The k-means algorithm: a comprehensive survey and performance evaluation. Electronics 9(8):1295","journal-title":"Electronics"},{"key":"1151_CR3","unstructured":"Bansal T, Pachocki J, Sidor S et al (2017) Emergent complexity via multi-agent competition. arXiv preprint arXiv:1710.03748"},{"issue":"2","key":"1151_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12650-021-00793-9","volume":"25","author":"S Cheng","year":"2022","unstructured":"Cheng S, Li X, Shan G, Niu B, Wang Y, Luo M (2022) ACMViz: a visual analytics approach to understand DRL-based autonomous control model. J Visualization 25(2):1\u201316","journal-title":"J Visualization"},{"issue":"1","key":"1151_CR5","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.visinf.2023.11.003","volume":"8","author":"J Dong","year":"2024","unstructured":"Dong J, Zhang H, Cui M, Lin Y, Wu H-Y, Bi C (2024) TCEVis: visual analytics of traffic congestion influencing factors based on explainable machine learning. Visual Inf 8(1):56\u201366. https:\/\/doi.org\/10.1016\/j.visinf.2023.11.003","journal-title":"Visual Inf"},{"key":"1151_CR6","unstructured":"Dulac-Arnold G, Levine N, Mankowitz DJ (2020) An empirical investigation of the challenges of real-world reinforcement learning. arXiv preprint arXiv:2003.11881"},{"issue":"1","key":"1151_CR7","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1109\/TVCG.2023.3327353","volume":"30","author":"L Gao","year":"2024","unstructured":"Gao L, Shao Z, Luo Z, Hu H, Turkay C, Chen S (2024) Transforlearn: interactive visual tutorial for the transformer model. IEEE Trans Visual Comput Graphics 30(1):891\u2013901","journal-title":"IEEE Trans Visual Comput Graphics"},{"issue":"8","key":"1151_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10994-024-06543-w","volume":"113","author":"C Glanois","year":"2024","unstructured":"Glanois C, Weng P, Zimmer M, Li D, Yang T, Hao J, Liu W (2024) A survey on interpretable reinforcement learning. Mach Learn 113(8):1\u201344","journal-title":"Mach Learn"},{"key":"1151_CR9","first-page":"12222","volume":"34","author":"W Guo","year":"2021","unstructured":"Guo W, Wu X, Khan U et al (2021) Edge: explaining deep reinforcement learning policies. Adv Neural Inf Process Syst 34:12222\u201312236","journal-title":"Adv Neural Inf Process Syst"},{"key":"1151_CR10","doi-asserted-by":"crossref","unstructured":"Guo Y, Jiang Z, Wang Y-J, Gao J, Chen J (2024) Decentralized motor skill learning for complex robotic systems. In: IEEE International Conference on Robotics and Automation","DOI":"10.1109\/LRA.2023.3301274"},{"issue":"1","key":"1151_CR11","doi-asserted-by":"publisher","first-page":"1032","DOI":"10.1109\/TVCG.2025.3634627","volume":"32","author":"P Hao","year":"2026","unstructured":"Hao P, Kang D, Hinds N, Wang Q (2026) FlowForge: guiding the creation of multi-agent workflows with design space visualization as a thinking scaffold. IEEE Trans Visual Comput Graph 32(1):1032\u20131042. https:\/\/doi.org\/10.1109\/TVCG.2025.3634627","journal-title":"IEEE Trans Visual Comput Graph"},{"key":"1151_CR12","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"7","key":"1151_CR13","first-page":"1102","volume":"33","author":"X Junxiao","year":"2021","unstructured":"Junxiao X, Xiangyan K, Yibo G, Aiguo L, Jian L, Xi W, Mingliang X (2021) Dynamic obstacle avoidance method for carrier aircraft based on deep reinforcement learning. J Comput-Aided Des Comput Graph 33(7):1102\u20131112","journal-title":"J Comput-Aided Des Comput Graph"},{"issue":"1","key":"1151_CR14","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1109\/TVCG.2018.2864500","volume":"25","author":"M Kahng","year":"2018","unstructured":"Kahng M, Thorat N, Chau DH et al (2018) Gan lab: understanding complex deep generative models using interactive visual experimentation. IEEE Trans Visual Comput Graphics 25(1):310\u2013320","journal-title":"IEEE Trans Visual Comput Graphics"},{"issue":"1","key":"1151_CR15","first-page":"1","volume":"17","author":"DJ Ketchen","year":"1996","unstructured":"Ketchen DJ, Shook CL (1996) The elbow method for finding the optimal number of clusters. Strateg Manag J 17(1):1\u201323","journal-title":"Strateg Manag J"},{"issue":"4","key":"1151_CR16","doi-asserted-by":"publisher","first-page":"4063","DOI":"10.1007\/s10489-022-03605-1","volume":"53","author":"T Kravaris","year":"2023","unstructured":"Kravaris T, Lentzos K, Santipantakis G et al (2023) Explaining deep reinforcement learning decisions in complex multiagent settings: towards enabling automation in air traffic flow management. Appl Intell 53(4):4063\u20134098","journal-title":"Appl Intell"},{"issue":"1","key":"1151_CR17","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1109\/TVCG.2018.2865027","volume":"25","author":"BC Kwon","year":"2019","unstructured":"Kwon BC, Choi M-J, Kim JT, Choi E, Kim YB, Kwon S, Sun J, Choo J (2019) Retainvis: visual analytics with interpretable and interactive recurrent neural networks on electronic medical records. IEEE Trans Visual Comput Graph 25(1):299\u2013309","journal-title":"IEEE Trans Visual Comput Graph"},{"key":"1151_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12650-025-01060-x","volume":"28","author":"H Liu","year":"2025","unstructured":"Liu H, Dai H, Xu J et al (2025) Tovis: a visual analytics system for outcome-driven treatment plan optimization. J Visual 28:1\u201318","journal-title":"J Visual"},{"issue":"8","key":"1151_CR19","doi-asserted-by":"publisher","first-page":"4182","DOI":"10.1109\/TVCG.2024.3394053","volume":"31","author":"J Lu","year":"2025","unstructured":"Lu J, Pan B, Chen J, Feng Y, Hu J, Peng Y, Chen W (2025) Agentlens: visual analysis for agent behaviors in llm-based autonomous systems. IEEE Trans Visual Comput Graph 31(8):4182\u20134197. https:\/\/doi.org\/10.1109\/TVCG.2024.3394053","journal-title":"IEEE Trans Visual Comput Graph"},{"issue":"11","key":"1151_CR20","first-page":"2579","volume":"9","author":"L Maaten","year":"2008","unstructured":"Maaten L, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9(11):2579\u20132605","journal-title":"J Mach Learn Res"},{"key":"1151_CR21","doi-asserted-by":"crossref","unstructured":"McInnes L, Healy J, Melville J (2018) Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426","DOI":"10.21105\/joss.00861"},{"key":"1151_CR22","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1111\/cgf.14839","volume":"42","author":"Y Metz","year":"2023","unstructured":"Metz Y, Bykovets E, Joos L et al (2023) Visitor: visual interactive state sequence exploration for reinforcement learning. Computer Graphics Forum 42:397\u2013408","journal-title":"Computer Graphics Forum"},{"key":"1151_CR23","unstructured":"Metz Y, Schlegel U, Seebacher D, El-Assady M, Keim DA (2022) A comprehensive workflow for effective imitation and reinforcement learning with visual analytics. EuroVis Workshop on Visual Analytics"},{"key":"1151_CR24","unstructured":"Rauber PE, Falcao AX, Telea AC et al (2016) Visualizing time-dependent data using dynamic t-sne. Eurographics Conference on Visualization"},{"key":"1151_CR25","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1111\/cgf.14733","volume":"42","author":"BL Rosa","year":"2023","unstructured":"Rosa BL, Blasilli G, Bourqui R et al (2023) State of the art of visual analytics for explainable deep learning. Comput Graph Forum 42:319\u2013355","journal-title":"Comput Graph Forum"},{"issue":"5","key":"1151_CR26","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1007\/s12650-023-00928-0","volume":"26","author":"X Shi","year":"2023","unstructured":"Shi X, Zhang J, Liang Z, Seng D (2023) Maddpgviz: a visual analytics approach to understand multi-agent deep reinforcement learning. J Visualization 26(5):1189\u20131205","journal-title":"J Visualization"},{"key":"1151_CR27","doi-asserted-by":"crossref","unstructured":"Todorov E, Erez T, Tassa Y (2012) Mujoco: A physics engine for model-based control. In: 2012 IEEE\/RSJ International Conference on Intelligent Robots and Systems. pp 5026\u20135033","DOI":"10.1109\/IROS.2012.6386109"},{"issue":"1","key":"1151_CR28","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1109\/TVCG.2018.2864504","volume":"25","author":"J Wang","year":"2018","unstructured":"Wang J, Gou L, Shen HW et al (2018) Dqnviz: a visual analytics approach to understand deep q-networks. IEEE Trans Visual Comput Graphics 25(1):288\u2013298","journal-title":"IEEE Trans Visual Comput Graphics"},{"issue":"12","key":"1151_CR29","doi-asserted-by":"publisher","first-page":"4141","DOI":"10.1109\/TVCG.2021.3076749","volume":"28","author":"J Wang","year":"2021","unstructured":"Wang J, Zhang W, Yang H et al (2021) Visual analytics for rnn-based deep reinforcement learning. IEEE Trans Visual Comput Graphics 28(12):4141\u20134155","journal-title":"IEEE Trans Visual Comput Graphics"},{"key":"1151_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/s12650-026-01128-2","author":"Y Wu","year":"2026","unstructured":"Wu Y, Tang T, Li Y, Wu Y (2026) CamouflageVis: a visual analytics approach for discovering camouflaged malicious entities in e-commerce. J Vis. https:\/\/doi.org\/10.1007\/s12650-026-01128-2","journal-title":"J Vis"},{"key":"1151_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/s12650-026-01122-8","author":"Q Xia","year":"2026","unstructured":"Xia Q, Wang X, Zhang H, Lin Y, Lv Z (2026) Visual analytics for taxi dispatching based on multi-agent reinforcement learning. J Vis. https:\/\/doi.org\/10.1007\/s12650-026-01122-8","journal-title":"J Vis"},{"key":"1151_CR32","doi-asserted-by":"crossref","unstructured":"Zhang Y, Zheng G, Liu Z et al (2024) Marlens: understanding multi-agent reinforcement learning for traffic signal control via visual analytics. IEEE Trans Visual Comput Graphics","DOI":"10.1109\/TVCG.2024.3392587"},{"issue":"9","key":"1151_CR33","doi-asserted-by":"publisher","first-page":"6162","DOI":"10.1109\/TVCG.2024.3496112","volume":"31","author":"Y Zhao","year":"2025","unstructured":"Zhao Y, Wang J, Xiang L, Zhang X, Guo Z, Turkay C, Zhang Y, Chen S (2025) LightVA: lightweight visual analytics with LLM agent-based task planning and execution. IEEE Trans Visual Comput Graph 31(9):6162\u20136177. https:\/\/doi.org\/10.1109\/TVCG.2024.3496112","journal-title":"IEEE Trans Visual Comput Graph"}],"container-title":["Journal of Visualization"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12650-026-01151-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12650-026-01151-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12650-026-01151-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T15:41:19Z","timestamp":1790696479000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12650-026-01151-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,9]]},"references-count":33,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,10]]}},"alternative-id":["1151"],"URL":"https:\/\/doi.org\/10.1007\/s12650-026-01151-3","relation":{},"ISSN":["1343-8875","1875-8975"],"issn-type":[{"value":"1343-8875","type":"print"},{"value":"1875-8975","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,9]]},"assertion":[{"value":"24 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 July 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 August 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}