{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T00:12:36Z","timestamp":1761178356496,"version":"build-2065373602"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T00:00:00Z","timestamp":1761091200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T00:00:00Z","timestamp":1761091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100019180","name":"HORIZON EUROPE European Research Council","doi-asserted-by":"publisher","award":["NANOVR 866559"],"award-info":[{"award-number":["NANOVR 866559"]}],"id":[{"id":"10.13039\/100019180","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010801","name":"Xunta de Galicia","doi-asserted-by":"publisher","award":["ED431G-2019\/04"],"award-info":[{"award-number":["ED431G-2019\/04"]}],"id":[{"id":"10.13039\/501100010801","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-025-04465-5","type":"journal-article","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T12:11:29Z","timestamp":1761135089000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-Guided Molecular Simulations in VR: Exploring Strategies for Imitation Learning in Hyperdimensional Molecular Systems"],"prefix":"10.1007","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4876-4686","authenticated-orcid":false,"given":"Mohamed","family":"Dhouioui","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0343-7796","authenticated-orcid":false,"given":"Jonathan","family":"Barnoud","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2535-7180","authenticated-orcid":false,"given":"Rhoslyn","family":"Roebuck Williams","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8440-2629","authenticated-orcid":false,"given":"Harry J.","family":"Stroud","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6010-1151","authenticated-orcid":false,"given":"Phil","family":"Bates","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9608-3845","authenticated-orcid":false,"given":"David R.","family":"Glowacki","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,22]]},"reference":[{"key":"4465_CR1","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.cpc.2017.11.006","volume":"224","author":"WR Saunders","year":"2018","unstructured":"Saunders WR, Grant J, M\u00fcller EH. A domain specific language for performance portable molecular dynamics algorithms. Comput Phys Commun. 2018;224:119\u201335. https:\/\/doi.org\/10.1016\/j.cpc.2017.11.006.","journal-title":"Comput Phys Commun"},{"key":"4465_CR2","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1080\/17460441.2022.2079632","volume":"17","author":"RK Walters","year":"2022","unstructured":"Walters RK, Gale EM, Barnoud J, Glowacki DR, Mulholland AJ. The emerging potential of interactive virtual reality in drug discovery. Expert Opin Drug Discov. 2022;17:685\u201398. https:\/\/doi.org\/10.1080\/17460441.2022.2079632.","journal-title":"Expert Opin Drug Discov"},{"issue":"22","key":"4465_CR3","doi-asserted-by":"publisher","DOI":"10.1063\/1.5092590","volume":"150","author":"MB O\u2019Connor","year":"2019","unstructured":"O\u2019Connor MB, Bennie SJ, Deeks HM, Jamieson-Binnie A, Jones AJ, Shannon RJ, et al. Interactive molecular dynamics in virtual reality from quantum chemistry to drug binding: An open-source multi-person framework. J Chem Phys. 2019;150(22):220901. https:\/\/doi.org\/10.1063\/1.5092590.","journal-title":"J Chem Phys"},{"issue":"3","key":"4465_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0228461","volume":"15","author":"HM Deeks","year":"2020","unstructured":"Deeks HM, Walters RK, Hare SR, O\u2019Connor MB, Mulholland AJ, Glowacki DR. Interactive molecular dynamics in virtual reality for accurate flexible protein-ligand docking. PLoS One. 2020;15(3):1\u201321. https:\/\/doi.org\/10.1371\/journal.pone.0228461.","journal-title":"PLoS One"},{"key":"4465_CR5","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c01030","author":"HM Deeks","year":"2020","unstructured":"Deeks HM, Walters RK, Barnoud J, Glowacki D, Mulholland A. Interactive molecular dynamics in virtual reality is an effective tool for flexible substrate and inhibitor docking to the SARS-CoV-2 main protease. J Chem Inf Model. 2020. https:\/\/doi.org\/10.1021\/acs.jcim.0c01030.","journal-title":"J Chem Inf Model"},{"key":"4465_CR6","doi-asserted-by":"publisher","first-page":"16665","DOI":"10.1038\/s41598-023-43523-x","volume":"13","author":"HM Deeks","year":"2023","unstructured":"Deeks HM, Zinovjev K, Barnoud J, Mulholland AJ, van der Kamp MW, Glowacki DR. Free energy along drug-protein binding pathways interactively sampled in virtual reality. Sci Rep. 2023;13:16665. https:\/\/doi.org\/10.1038\/s41598-023-43523-x.","journal-title":"Sci Rep"},{"key":"4465_CR7","doi-asserted-by":"publisher","DOI":"10.1063\/5.0062517","volume":"155","author":"RJ Shannon","year":"2021","unstructured":"Shannon RJ, Deeks HM, Burfoot E, Clark E, Jones AJ, Mulholland AJ, et al. Exploring human-guided strategies for reaction network exploration: interactive molecular dynamics in virtual reality as a tool for citizen scientists. J Chem Phys. 2021;155:154106. https:\/\/doi.org\/10.1063\/5.0062517.","journal-title":"J Chem Phys"},{"issue":"5","key":"4465_CR8","doi-asserted-by":"publisher","first-page":"6322","DOI":"10.1109\/TNNLS.2022.3213246","volume":"35","author":"B Zheng","year":"2024","unstructured":"Zheng B, Verma S, Zhou J, Tsang IW, Chen F. Imitation learning: Progress, taxonomies and challenges. IEEE Trans Neural Netwo Learn Syst. 2024;35(5):6322\u201337. https:\/\/doi.org\/10.1109\/TNNLS.2022.3213246.","journal-title":"IEEE Trans Neural Netwo Learn Syst"},{"key":"4465_CR9","doi-asserted-by":"publisher","DOI":"10.1145\/3054912","author":"A Hussein","year":"2017","unstructured":"Hussein A, Gaber MM, Elyan E, Jayne C. Imitation learning: a survey of learning methods. ACM Comput Surv. 2017. https:\/\/doi.org\/10.1145\/3054912.","journal-title":"ACM Comput Surv"},{"key":"4465_CR10","unstructured":"Gavenski N, Meneguzzi F, Luck M, Rodrigues O. A survey of imitation learning methods, environments and metrics 2024. arXiv:2404.19456."},{"key":"4465_CR11","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/S1364-6613(99)01327-3","volume":"3","author":"S Schaal","year":"1999","unstructured":"Schaal S. Is imitation learning the route to humanoid robots? Trends Cogn Sci. 1999;3:233\u201342. https:\/\/doi.org\/10.1016\/S1364-6613(99)01327-3.","journal-title":"Trends Cogn Sci"},{"key":"4465_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/frobt.2018.00001","volume":"5","author":"JA Reggia","year":"2018","unstructured":"Reggia JA, Katz GE, Davis GP. Humanoid cognitive robots that learn by imitating: implications for consciousness studies. Front Robot AI. 2018;5:1. https:\/\/doi.org\/10.3389\/frobt.2018.00001.","journal-title":"Front Robot AI"},{"key":"4465_CR13","doi-asserted-by":"publisher","first-page":"1278","DOI":"10.3390\/s21041278","volume":"21","author":"J Hua","year":"2021","unstructured":"Hua J, Zeng L, Li G, Ju Z. Learning for a robot: deep reinforcement learning, imitation learning, transfer learning. Sensors. 2021;21:1278. https:\/\/doi.org\/10.3390\/s21041278.","journal-title":"Sensors"},{"key":"4465_CR14","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2024.3395626","author":"M Zare","year":"2023","unstructured":"Zare M, Kebria PM, Khosravi A, Nahavandi S. A survey of imitation learning: algorithms, recent developments, and challenges. IEEE Trans Cybern. 2023. https:\/\/doi.org\/10.1109\/TCYB.2024.3395626.","journal-title":"IEEE Trans Cybern"},{"issue":"36","key":"4465_CR15","doi-asserted-by":"publisher","first-page":"eabb6987","DOI":"10.1126\/sciadv.abb6987","volume":"6","author":"P Leinen","year":"2020","unstructured":"Leinen P, Esders M, Sch\u00fctt KT, Wagner C, M\u00fcller KR, Tautz FS. Autonomous robotic nanofabrication with reinforcement learning. Sci Adv. 2020;6(36):eabb6987. https:\/\/doi.org\/10.1126\/sciadv.abb6987.","journal-title":"Sci Adv"},{"issue":"6","key":"4465_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pcbi.1012229","volume":"20","author":"C Ai","year":"2024","unstructured":"Ai C, Yang H, Liu X, Dong R, Ding Y, Guo F. Mtmol-gpt: de novo multi-target molecular generation with transformer-based generative adversarial imitation learning. PLoS Comput Biol. 2024;20(6):1\u201323. https:\/\/doi.org\/10.1371\/journal.pcbi.1012229.","journal-title":"PLoS Comput Biol"},{"key":"4465_CR17","unstructured":"Jia X, Blessing D, Jiang X, Reuss M, Donat A, Lioutikov R, Neumann G. Towards diverse behaviors: a benchmark for imitation learning with human demonstrations. In: The Twelfth International Conference on Learning Representations. 2024. arXiv:2402.14606."},{"issue":"4","key":"4465_CR18","doi-asserted-by":"publisher","first-page":"2121","DOI":"10.3390\/ijerph18042121","volume":"18","author":"M Maadi","year":"2021","unstructured":"Maadi M, Akbarzadeh Khorshidi H, Aickelin U. A review on human\u2013AI interaction in machine learning and insights for medical applications. Int J Environ Res Public Health. 2021;18(4):2121. https:\/\/doi.org\/10.3390\/ijerph18042121.","journal-title":"Int J Environ Res Public Health"},{"key":"4465_CR19","doi-asserted-by":"publisher","first-page":"2109","DOI":"10.1007\/s11423-020-09858-2","volume":"69","author":"ME Webb","year":"2021","unstructured":"Webb ME, Fluck A, Magenheim J, Malyn-Smith J, Waters J, Desch\u00eanes M, et al. Machine learning for human learners: opportunities, issues, tensions and threats. Educ Technol Res Dev. 2021;69:2109\u201330. https:\/\/doi.org\/10.1007\/s11423-020-09858-2.","journal-title":"Educ Technol Res Dev"},{"key":"4465_CR20","doi-asserted-by":"publisher","DOI":"10.3390\/s21103409","author":"E Jung","year":"2021","unstructured":"Jung E, Kim I. Hybrid imitation learning framework for robotic manipulation tasks. Sensors. 2021. https:\/\/doi.org\/10.3390\/s21103409.","journal-title":"Sensors"},{"key":"4465_CR21","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jchemed.1c00654","author":"S Seritan","year":"2021","unstructured":"Seritan S, Wang Y, Ford JE, Valentini A, Gold T, Mart\u00ednez TJ. Interachem: virtual reality visualizer for reactive interactive molecular dynamics. J Chem Educ. 2021. https:\/\/doi.org\/10.1021\/acs.jchemed.1c00654.","journal-title":"J Chem Educ"},{"key":"4465_CR22","doi-asserted-by":"publisher","unstructured":"Doutreligne S, Gageat C, Cragnolini T, Taly A, Pasquali S, Derreumaux P, Baaden M. UnityMol: interactive and ludic visual manipulation of coarse-grained RNA and other biomolecules. In: 2015 IEEE 1st international workshop on virtual and augmented reality for molecular science (VARMS@IEEEVR); 2015. p. 1\u20136. https:\/\/doi.org\/10.1109\/VARMS.2015.7151718.","DOI":"10.1109\/VARMS.2015.7151718"},{"key":"4465_CR23","doi-asserted-by":"publisher","unstructured":"Bennie SJ, Maritan M, Gast J, Loschen M, Gruffat D, Bartolotta R, Hessenauer S, Leija E, McCloskey S. A virtual and mixed reality platform for molecular design and drug discovery\u2014Nanome Version 1.24. In: By\u0161ka J, Krone M, Sommer B editors. Workshop on molecular graphics and visual analysis of molecular data. The Eurographics Association; 2023. https:\/\/doi.org\/10.2312\/molva.20231114.","DOI":"10.2312\/molva.20231114"},{"key":"4465_CR24","doi-asserted-by":"publisher","first-page":"17366","DOI":"10.1021\/ACS.JMEDCHEM.1C01475","volume":"64","author":"DW Kneller","year":"2021","unstructured":"Kneller DW, Li H, Galanie S, Phillips G, Labb\u00e9 A, Weiss KL, et al. Structural, electronic, and electrostatic determinants for inhibitor binding to subsites s1 and s2 in sars-cov-2 main protease. J Med Chem. 2021;64:17366\u201383. https:\/\/doi.org\/10.1021\/ACS.JMEDCHEM.1C01475.","journal-title":"J Med Chem"},{"issue":"3","key":"4465_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pcbi.1007747","volume":"16","author":"KC Cassidy","year":"2020","unstructured":"Cassidy KC, \u0160ef\u010d\u00edk J, Raghav Y, Chang A, Durrant JD. Proteinvr: web-based molecular visualization in virtual reality. PLoS Comput Biol. 2020;16(3):1\u201317. https:\/\/doi.org\/10.1371\/journal.pcbi.1007747.","journal-title":"PLoS Comput Biol"},{"issue":"11","key":"4465_CR26","doi-asserted-by":"publisher","first-page":"2475","DOI":"10.1021\/acs.jcim.5b00544","volume":"55","author":"M Norrby","year":"2015","unstructured":"Norrby M, Grebner C, Eriksson J, Bostr\u00f6m J. Molecular rift: virtual reality for drug designers. J Chem Inf Model. 2015;55(11):2475\u201384. https:\/\/doi.org\/10.1021\/acs.jcim.5b00544.","journal-title":"J Chem Inf Model"},{"key":"4465_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmgm.2023.108606","volume":"125","author":"J Crossley-Lewis","year":"2023","unstructured":"Crossley-Lewis J, Dunn J, Buda C, Sunley GJ, Elena AM, Todorov IT, et al. Interactive molecular dynamics in virtual reality for modelling materials and catalysts. J Mol Graph Model. 2023;125:108606. https:\/\/doi.org\/10.1016\/j.jmgm.2023.108606.","journal-title":"J Mol Graph Model"},{"issue":"110","key":"4465_CR28","doi-asserted-by":"publisher","first-page":"8118","DOI":"10.21105\/joss.08118","volume":"10","author":"HJ Stroud","year":"2025","unstructured":"Stroud HJ, Wonnacott MD, Barnoud J, Roebuck Williams R, Dhouioui M, McSloy A, et al. NanoVer server: a python package for serving real-time multi-user interactive molecular dynamics in virtual reality. J Open Sour Softw. 2025;10(110):8118. https:\/\/doi.org\/10.21105\/joss.08118.","journal-title":"J Open Sour Softw"},{"key":"4465_CR29","doi-asserted-by":"publisher","unstructured":"Jamieson-Binnie AD, O\u2019Connor MB, Barnoud J, Wonnacott MD, Bennie SJ, Glowacki DR. Narupa iMD: a VR-enabled multiplayer framework for streaming interactive molecular simulations. In: ACM SIGGRAPH 2020 immersive pavilion, SIGGRAPH \u201920. New York: Association for Computing Machinery; 2020. https:\/\/doi.org\/10.1145\/3388536.3407891.","DOI":"10.1145\/3388536.3407891"},{"key":"4465_CR30","doi-asserted-by":"publisher","unstructured":"Gowers RJ, Linke M, Barnoud J, Reddy TJE, Melo MN, Seyler SL, Doma\u0144ski J, Dotson, S\u00e9bastien Buchoux DL, Kenney IM, Beckstein O. MDAnalysis: a python package for the rapid analysis of molecular dynamics simulations. In: Sebastian B, Scott R editors. Proceedings of the 15th Python in science conference. 2016. p. 98\u2013105. https:\/\/doi.org\/10.25080\/Majora-629e541a-00e.","DOI":"10.25080\/Majora-629e541a-00e"},{"issue":"10","key":"4465_CR31","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1002\/jcc.21787","volume":"32","author":"N Michaud-Agrawal","year":"2011","unstructured":"Michaud-Agrawal N, Denning EJ, Woolf TB, Beckstein O. Mdanalysis: a toolkit for the analysis of molecular dynamics simulations. J Comput Chem. 2011;32(10):2319\u201327. https:\/\/doi.org\/10.1002\/jcc.21787.","journal-title":"J Comput Chem"},{"issue":"2","key":"4465_CR32","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1021\/jp035828x","volume":"108","author":"A Kalra","year":"2004","unstructured":"Kalra A, Hummer G, Garde S. Methane partitioning and transport in hydrated carbon nanotubes. J Phys Chem B. 2004;108(2):544\u20139. https:\/\/doi.org\/10.1021\/jp035828x.","journal-title":"J Phys Chem B"},{"key":"4465_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2024.104812","volume":"182","author":"A Correia","year":"2024","unstructured":"Correia A, Alexandre LA. A survey of demonstration learning. Robot Auton Syst. 2024;182:104812. https:\/\/doi.org\/10.1016\/j.robot.2024.104812.","journal-title":"Robot Auton Syst"},{"key":"4465_CR34","unstructured":"Bui TV, Mai TA, Nguyen TH. Inverse factorized soft Q-learning for cooperative multi-agent imitation learning. In: The thirty-eighth annual conference on neural information processing systems. 2024. https:\/\/openreview.net\/forum?id=xrbgXJomJp."},{"key":"4465_CR35","unstructured":"Bui TV, Mai T, Nguyen TH. Inverse factorized q-learning for cooperative multi-agent imitation learning. 2023. arXiv:2310.06801."},{"key":"4465_CR36","doi-asserted-by":"publisher","unstructured":"Ellis B, Cook J, Moalla S, Samvelyan M, Sun M, Mahajan A, Foerster JN, Whiteson S. SMACv2: an improved benchmark for cooperative multi-agent reinforcement learning. In: Proceedings of the 37th international conference on neural information processing systems, NIPS \u201923. Red Hook: Curran Associates Inc.; 2024. https:\/\/doi.org\/10.5555\/3666122.3667756.","DOI":"10.5555\/3666122.3667756"},{"key":"4465_CR37","unstructured":"FPT. Fpt reinforcement learning competition. 2020. https:\/\/codelearn.io\/game\/detail\/2212875#ai-game-summary."},{"key":"4465_CR38","unstructured":"Paine TL, Gulcehre C, Shahriari B, Denil M, Hoffman M, Soyer H, Tanburn R, Kapturowski S, Rabinowitz N, Williams D, et\u00a0al. Scaling laws for imitation learning in single-agent games. 2023. arXiv preprint arXiv:2301.13314."},{"key":"4465_CR39","unstructured":"K\u00fcttler H, Nardelli N, Miller AH, Raileanu R, Selvatici M, Grefenstette E, Rockt\u00e4schel T. The nethack learning environment. CoRR. 2020. arXiv:2006.13760."},{"key":"4465_CR40","doi-asserted-by":"publisher","DOI":"10.1145\/3606926","author":"M Younes","year":"2023","unstructured":"Younes M, Kijak E, Kulpa R, Malinowski S, Multon F. Maaip: multi-agent adversarial interaction priors for imitation from fighting demonstrations for physics-based characters. Proc ACM Comput Graph Interact Tech. 2023. https:\/\/doi.org\/10.1145\/3606926.","journal-title":"Proc ACM Comput Graph Interact Tech"},{"issue":"1","key":"4465_CR41","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1146\/annurev-control-100819-063206","volume":"3","author":"H Ravichandar","year":"2020","unstructured":"Ravichandar H, Polydoros AS, Chernova S, Billard A. Recent advances in robot learning from demonstration. Ann Rev Control Robot Auton Syst. 2020;3(1):297\u2013330. https:\/\/doi.org\/10.1146\/annurev-control-100819-063206.","journal-title":"Ann Rev Control Robot Auton Syst"},{"key":"4465_CR42","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.48550\/arXiv.2210.11339","volume":"205","author":"Y Zhu","year":"2022","unstructured":"Zhu Y, Joshi A, Stone P, Zhu Y. Viola: imitation learning for vision-based manipulation with object proposal priors. Proc Mach Learn Res. 2022;205:1199\u2013210. https:\/\/doi.org\/10.48550\/arXiv.2210.11339.","journal-title":"Proc Mach Learn Res"},{"key":"4465_CR43","doi-asserted-by":"publisher","unstructured":"Seo M, Gupta R, Zhu Y, Skoutnev A, Sentis L, Zhu Y. Learning to walk by steering: perceptive quadrupedal locomotion in dynamic environments. In: 2023 IEEE international conference on robotics and automation (ICRA). 2023. p. 5099\u2013105. https:\/\/doi.org\/10.1109\/ICRA48891.2023.10161302.","DOI":"10.1109\/ICRA48891.2023.10161302"},{"key":"4465_CR44","doi-asserted-by":"publisher","DOI":"10.1145\/3623384","author":"SA Mehta","year":"2023","unstructured":"Mehta SA, Losey DP. Unified learning from demonstrations, corrections, and preferences during physical human\u2013robot interaction. J Hum Robot Interact. 2023. https:\/\/doi.org\/10.1145\/3623384.","journal-title":"J Hum Robot Interact"},{"key":"4465_CR45","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1162\/NECO.1991.3.1.88","volume":"3","author":"DA Pomerleau","year":"1991","unstructured":"Pomerleau DA. Efficient training of artificial neural networks for autonomous navigation. Neural Comput. 1991;3:88\u201397. https:\/\/doi.org\/10.1162\/NECO.1991.3.1.88.","journal-title":"Neural Comput"},{"key":"4465_CR46","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-30164-8_69","author":"C Sammut","year":"2011","unstructured":"Sammut C. Behavioral cloning. Encycl Mach Learn. 2011. https:\/\/doi.org\/10.1007\/978-0-387-30164-8_69.","journal-title":"Encycl Mach Learn"},{"key":"4465_CR47","doi-asserted-by":"publisher","unstructured":"Russell S. Learning agents for uncertain environments (extended abstract). In: Proceedings of the eleventh annual conference on computational learning theory, COLT\u2019 98. New York: Association for Computing Machinery; 1998. p. 101\u201303. https:\/\/doi.org\/10.1145\/279943.279964.","DOI":"10.1145\/279943.279964"},{"key":"4465_CR48","doi-asserted-by":"publisher","unstructured":"Ng AY, Russell SJ. Algorithms for inverse reinforcement learning. In: Proceedings of the seventeenth international conference on machine learning, ICML \u201900. San Francisco: Morgan Kaufmann Publishers Inc.; 2000. p. 663\u201370. https:\/\/doi.org\/10.5555\/645529.657801.","DOI":"10.5555\/645529.657801"},{"key":"4465_CR49","doi-asserted-by":"publisher","unstructured":"Ziebart BD, Maas A, Bagnell JA, Dey AK. Maximum entropy inverse reinforcement learning. In: Proceedings of the 23rd National conference on artificial intelligence\u2014volume 3, AAAI\u201908. AAAI Press; 2008. p. 1433\u201338. https:\/\/doi.org\/10.5555\/1625275.1625692.","DOI":"10.5555\/1625275.1625692"},{"key":"4465_CR50","doi-asserted-by":"publisher","unstructured":"Ramachandran D, Amir E. Bayesian inverse reinforcement learning. In: Proceedings of the 20th international joint conference on artifical intelligence, IJCAI\u201907. San Francisco: Morgan Kaufmann Publishers Inc.; 2007. p. 2586\u201391. https:\/\/doi.org\/10.5555\/1625275.1625692.","DOI":"10.5555\/1625275.1625692"},{"key":"4465_CR51","unstructured":"Metelli AM, Ramponi G, Concetti A, Restelli M. Provably efficient learning of transferable rewards. In: Meila M, Zhang T editors. Proceedings of the 38th international conference on machine learning, proceedings of machine learning research, vol. 139. PMLR; 2021. p. 7665\u201376. https:\/\/proceedings.mlr.press\/v139\/metelli21a.html."},{"key":"4465_CR52","unstructured":"Deka A, Liu C, Sycara KP. ARC\u2014Actor residual critic for adversarial imitation learning. In: Liu K, Kulic D, Ichnowski J, editors. Proceedings of the 6th conference on robot learning, proceedings of machine learning research, vol. 205. PMLR; 2023. p. 1446\u201356. https:\/\/proceedings.mlr.press\/v205\/deka23a.html."},{"key":"4465_CR53","doi-asserted-by":"publisher","unstructured":"Ho J, Ermon S. Generative adversarial imitation learning. In: Proceedings of the 30th international conference on neural information processing systems, NIPS\u201916. Red Hook: Curran Associates Inc.; 2016. p. 4572\u201380. https:\/\/doi.org\/10.5555\/3157382.3157608.","DOI":"10.5555\/3157382.3157608"},{"key":"4465_CR54","unstructured":"Schulman J, Levine S, Abbeel P, Jordan M, Moritz P. Trust region policy optimization. In: Bach F, Blei D editors. Proceedings of the 32nd international conference on machine learning, proceedings of machine learning research, vol.\u00a037. Lille: PMLR; 2015. p. 1889\u201397. https:\/\/proceedings.mlr.press\/v37\/schulman15.html."},{"key":"4465_CR55","doi-asserted-by":"publisher","unstructured":"Pomerleau DA. ALVINN: an autonomous land vehicle in a neural network. In: Proceedings of the 2nd international conference on neural information processing systems, NIPS\u201988. Cambridge: MIT Press; 1988. p. 305\u201313. https:\/\/doi.org\/10.5555\/2969735.2969771.","DOI":"10.5555\/2969735.2969771"},{"key":"4465_CR56","doi-asserted-by":"publisher","unstructured":"de\u00a0Haan P, Jayaraman D, Levine S. Causal confusion in imitation learning. In: Proceedings of the 33rd international conference on neural information processing systems. Red Hook: Curran Associates Inc.; 2019.https:\/\/doi.org\/10.5555\/3454287.3455336.","DOI":"10.5555\/3454287.3455336"},{"key":"4465_CR57","unstructured":"Wen C, Lin J, Darrell T, Jayaraman D, Gao Y. Fighting copycat agents in behavioral cloning from observation histories. CoRR 2020. arXiv:2010.14876."},{"key":"4465_CR58","unstructured":"Spencer JC, Choudhury S, Venkatraman A, Ziebart BD, Bagnell JA. Feedback in imitation learning: The three regimes of covariate shift. CoRR 2021. arXiv:2102.02872."},{"key":"4465_CR59","unstructured":"Ross S, Gordon G, Bagnell D. A reduction of imitation learning and structured prediction to no-regret online learning. In: Gordon G, Dunson D, Dud\u00edk M, editors. Proceedings of the fourteenth international conference on artificial intelligence and statistics, proceedings of machine learning research, vol.\u00a015. Fort Lauderdale: PMLR; 2011. p. 627\u201335. https:\/\/proceedings.mlr.press\/v15\/ross11a.html."},{"key":"4465_CR60","unstructured":"Chang JD, Uehara M, Sreenivas D, Kidambi R, Sun W. Mitigating covariate shift in imitation learning via offline data with partial coverage. In: Beygelzimer A, Dauphin Y, Liang P, Vaughan JW editors. Advances in neural information processing systems. 2021. https:\/\/openreview.net\/forum?id=7PkfLkyLMRM."},{"key":"4465_CR61","unstructured":"Swamy G, Choudhury S, Bagnell JA, Wu ZS. Causal imitation learning under temporally correlated noise. CoRR 2022. arXiv:2202.01312."},{"key":"4465_CR62","unstructured":"Ruan K, Zhang J, Di X, Bareinboim E. Causal imitation learning via inverse reinforcement learning. In: The Eleventh international conference on learning representations. 2023. https:\/\/openreview.net\/forum?id=B-z41MBL_tH."},{"key":"4465_CR63","doi-asserted-by":"publisher","unstructured":"Chuang CC, Yang D, Wen C, Gao Y. Resolving copycat problems in visual imitation learning via residual action prediction. In: Computer Vision\u2014ECCV 2022, Computer Vision\u2014ECCV 2022. Cham: Springer Nature; 2022. p. 392\u2013409. https:\/\/doi.org\/10.1007\/978-3-031-19842-7_23.","DOI":"10.1007\/978-3-031-19842-7_23"},{"key":"4465_CR64","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmech.2021.113705","volume":"224","author":"VT Sabe","year":"2021","unstructured":"Sabe VT, Ntombela T, Jhamba LA, Maguire GEM, Govender T, Naicker T, et al. Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: a review. Eur J Med Chem. 2021;224:113705. https:\/\/doi.org\/10.1016\/j.ejmech.2021.113705.","journal-title":"Eur J Med Chem"},{"key":"4465_CR65","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104851","volume":"137","author":"B Shaker","year":"2021","unstructured":"Shaker B, Ahmad S, Lee J, Jung C, Na D. In silico methods and tools for drug discovery. Comput Biol Med. 2021;137:104851. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104851.","journal-title":"Comput Biol Med"},{"issue":"6631","key":"4465_CR66","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1126\/science.abq1347","volume":"379","author":"A Kondori","year":"2023","unstructured":"Kondori A, Esmaeilirad M, Harzandi AM, Amine R, Saray MT, Yu L, et al. A room temperature rechargeable li2o-based lithium-air battery enabled by a solid electrolyte. Science. 2023;379(6631):499\u2013505. https:\/\/doi.org\/10.1126\/science.abq1347.","journal-title":"Science"},{"key":"4465_CR67","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1007\/978-3-031-71707-9_13","volume-title":"Extended reality","author":"R Roebuck Williams","year":"2024","unstructured":"Roebuck Williams R, Barnoud J, Toledo L, Holzapfel T, Glowacki DR. Measuring the limit of perception of bond stiffness of interactive molecules in VR via a gamified psychophysics experiment. In: De Paolis LT, Arpaia P, Sacco M, editors. Extended reality. Cham: Springer Nature; 2024. p. 190\u20138. https:\/\/doi.org\/10.1007\/978-3-031-71707-9_13."}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-04465-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-025-04465-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-04465-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T12:11:32Z","timestamp":1761135092000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-025-04465-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,22]]},"references-count":67,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["4465"],"URL":"https:\/\/doi.org\/10.1007\/s42979-025-04465-5","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,22]]},"assertion":[{"value":"12 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"On behalf of all authors, the corresponding author states that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research involving human and\/or animals"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"922"}}