{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T19:57:48Z","timestamp":1767902268560,"version":"3.49.0"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031218668","type":"print"},{"value":"9783031218675","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-21867-5_17","type":"book-chapter","created":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T11:04:07Z","timestamp":1670929447000},"page":"256-270","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Deep Reinforcement Learning with\u00a0a\u00a0Classifier System \u2013 First Steps"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9298-6641","authenticated-orcid":false,"given":"Connor","family":"Sch\u00f6nberner","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5825-8915","authenticated-orcid":false,"given":"Sven","family":"Tomforde","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,14]]},"reference":[{"key":"17_CR1","unstructured":"Bishop, J.: xcsfrl. https:\/\/github.com\/jtbish\/xcsfrl. Accessed 09 May 2022"},{"key":"17_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1007\/978-3-030-58115-2_33","volume-title":"Parallel Problem Solving from Nature \u2013 PPSN XVI","author":"JT Bishop","year":"2020","unstructured":"Bishop, J.T., Gallagher, M.: Optimality-based analysis of XCSF compaction in discrete reinforcement learning. In: B\u00e4ck, T., et al. (eds.) PPSN 2020. LNCS, vol. 12270, pp. 471\u2013484. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58115-2_33"},{"key":"17_CR3","doi-asserted-by":"crossref","unstructured":"Bodnar, C., Day, B., Li\u00f3, P.: Proximal distilled evolutionary reinforcement learning. In: Proceedings of AAAI Conference on AI, vol. 34(4), pp. 3283\u20133290 (2020)","DOI":"10.1609\/aaai.v34i04.5728"},{"key":"17_CR4","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1007\/978-3-319-59650-1_52","volume-title":"Hybrid Artificial Intelligent Systems","author":"S-J Bu","year":"2017","unstructured":"Bu, S.-J., Cho, S.-B.: A hybrid system of deep learning and learning classifier system for database intrusion detection. In: Mart\u00ednez de Pis\u00f3n, F.J., Urraca, R., Quinti\u00e1n, H., Corchado, E. (eds.) HAIS 2017. LNCS (LNAI), vol. 10334, pp. 615\u2013625. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59650-1_52"},{"key":"17_CR5","unstructured":"Bull, L., O\u2019Hara, T.: Accuracy-based neuro and neuro-fuzzy classifier systems. In: Proceedings of GECCO 2002, p. 7 (2002)"},{"key":"17_CR6","doi-asserted-by":"publisher","unstructured":"Butz, M., Wilson, S.W.: An algorithmic description of XCS. In: Revised Papers from the 3rd IWLCS, pp. 253\u2013272. IWLCS 2000, Springer (2000). https:\/\/doi.org\/10.1007\/s005000100111","DOI":"10.1007\/s005000100111"},{"issue":"1","key":"17_CR7","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1109\/TEVC.2003.818194","volume":"8","author":"M Butz","year":"2004","unstructured":"Butz, M., Kovacs, T., Lanzi, P., Wilson, S.: Toward a theory of generalization and learning in XCS. IEEE Trans. on Evol. Comp. 8(1), 28\u201346 (2004)","journal-title":"IEEE Trans. on Evol. Comp."},{"issue":"1","key":"17_CR8","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1109\/TKDE.2007.190671","volume":"20","author":"H Dam","year":"2008","unstructured":"Dam, H., Abbass, H., Lokan, C.: Xin Yao: neural-based learning classifier systems. IEEE Trans. Knowl. Data Eng. 20(1), 26\u201339 (2008)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Giani, A., Baiardi, F., Starita, A.: PANIC: a parallel evolutionary rule based system. In: Proceedings of (EP)95, pp. 753\u2013771 MIT Press (1995)","DOI":"10.1007\/978-3-7091-7535-4_98"},{"key":"17_CR10","unstructured":"Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University of Michigan Press, Ann Arbor (1975)"},{"key":"17_CR11","unstructured":"Holland, J.H., Reitman, J.S.: Cognitive systems based on adaptive algorithms. In: Waterman, D., Haey-Roth, F. (eds.) Pattern-directed inference systems, pp. 313\u2013329. Academic Press (1978)"},{"key":"17_CR12","unstructured":"Lanzi, P.L., Loiacono, D.: XCSF with neural prediction. In: 2006 IEEE International Conference on Evolutionary Computation, pp. 2270\u20132276. IEEE (2006)"},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Lanzi, P.L., Loiacono, D.: Classifier systems that compute action mappings. In: GECCO 2007: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 1822\u20131829 (2007)","DOI":"10.1145\/1276958.1277322"},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"Lanzi, P.L., Loiacono, D., Wilson, S.W., Goldberg, D.E.: Extending XCSF beyond linear approximation. In: GECCO 2005: Proceedings of the 7th annual conference on Genetic and evolutionary computation, pp. 1827\u20131834 (2005)","DOI":"10.1145\/1068009.1068319"},{"key":"17_CR15","unstructured":"Loiacono, D., Lanzi, P.L.: Evolving neural networks for classifier prediction with XCSF. Technical report, AIRLab, Milano, Italy and IlliGAL, University of Illinois at Urbana Champaign (2014)"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"M\u00fcller-Schloer, C., Tomforde, S.: Organic Computing - Technical Systems for Survival in the Real World. Birkh\u00e4user (2017)","DOI":"10.1007\/978-3-319-68477-2"},{"key":"17_CR17","unstructured":"O\u2019Hara, T., Bull, L.: Prediction calculation in accuracy-based neural learning classifier systems. UWELCSG 04\u2013004, UWE Bristol, England (2004)"},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"O\u2019Hara, T., Bull, L.: A memetic accuracy-based neural learning classifier system. In: Proceedings of CEC05, vol. 3, pp. 2040\u20132045. IEEE (2005)","DOI":"10.1109\/CEC.2005.1554946"},{"key":"17_CR19","unstructured":"O\u2019Hara, T., Bull, L.: Building anticipations in an accuracy-based learning classifier system by use of an artificial neural network. In: 2005 IEEE Congress on Evolutionary Computation, vol. 3, pp. 2046\u20132052 (2005). ISSN: 1941\u20130026"},{"key":"17_CR20","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/978-3-540-71231-2_3","volume-title":"Learning Classifier Systems","author":"T O\u2019Hara","year":"2007","unstructured":"O\u2019Hara, T., Bull, L.: Backpropagation in accuracy-based neural learning classifier systems. In: Kovacs, T., Llor\u00e0, X., Takadama, K., Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) Learning Classifier Systems, pp. 25\u201339. Springer, Cham (2007)"},{"key":"17_CR21","unstructured":"OpenAI: frozen lake - gym documentation. https:\/\/www.gymlibrary.ml\/environments\/toy_text\/frozen_lake\/. Accessed 15 May 2022"},{"key":"17_CR22","unstructured":"Preen, R.J., Bull, L.: Deep learning with a classifier system: initial results. arXiv:2103.01118 [cs] (2021)"},{"key":"17_CR23","unstructured":"Preen, R.J., P\u00e4tzel, D.: XCSF. https:\/\/github.com\/rpreen\/xcsf (2021). Accessed 03 May 2022"},{"key":"17_CR24","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.1109\/TEVC.2021.3079320","volume":"25","author":"RJ Preen","year":"2021","unstructured":"Preen, R.J., Wilson, S.W., Bull, L.: Autoencoding with a classifier system. IEEE Trans. Evol. Comput. 25, 1079\u20131090 (2021)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"17_CR25","doi-asserted-by":"publisher","unstructured":"Prothmann, H., Tomforde, S., Branke, J., H\u00e4hner, J., M\u00fcller-Schloer, C., Schmeck, H.: Organic Traffic Control. In: M\u00fcller-Schloer, C., Schmeck, H., Ungerer, T. (eds.) Organic Computing \u2014 A Paradigm Shift for Complex Systems, vol. 1, pp. 431\u2013446. Springer, Cham (2011). https:\/\/doi.org\/10.1007\/978-3-0348-0130-0_28","DOI":"10.1007\/978-3-0348-0130-0_28"},{"key":"17_CR26","doi-asserted-by":"crossref","unstructured":"Rosenbauer, L., Stein, A., Maier, R., P\u00e4tzel, D., H\u00e4hner, J.: XCS as a reinforcement learning approach to automatic test case prioritization. In: Proceedings of GECCO 2020, pp. 1798\u20131806 (2020)","DOI":"10.1145\/3377929.3398128"},{"key":"17_CR27","unstructured":"Sch\u00f6nberner, C.: Deep Reinforcement Learning with a Classifier System. Master\u2019s thesis, Kiel University, Kiel, Germany (2022)"},{"key":"17_CR28","doi-asserted-by":"crossref","unstructured":"Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., et al.: Mastering the game of go without human knowledge. Nature 550(7676), 354\u2013359. Nature Publishing Group (2017)","DOI":"10.1038\/nature24270"},{"key":"17_CR29","doi-asserted-by":"crossref","unstructured":"Stein, A., Maier, R., Rosenbauer, L., H\u00e4hner, J.: XCS classifier system with experience replay. In: Proceedings of GECCO20, pp. 404\u2013413. ACM (2020)","DOI":"10.1145\/3377930.3390249"},{"key":"17_CR30","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.sysarc.2017.01.010","volume":"75","author":"A Stein","year":"2017","unstructured":"Stein, A., Rauh, D., Tomforde, S., H\u00e4hner, J.: Interpolation in the extended classifier system: An architectural perspective. J. Sys. Arch. 75, 79\u201394 (2017)","journal-title":"J. Sys. Arch."},{"key":"17_CR31","doi-asserted-by":"crossref","unstructured":"Stein, A., Rudolph, S., Tomforde, S., H\u00e4hner, J.: Self-learning smart cameras - harnessing the generalization capability of XCS. In: IJCCI17, pp. 129\u2013140 (2017)","DOI":"10.5220\/0006512101290140"},{"key":"17_CR32","doi-asserted-by":"crossref","unstructured":"Tomforde, S., H\u00e4hner, J.: Organic network control: turning standard protocols into evolving systems. In: Lio, P., Verma, D. (eds.) Biologically Inspired Networking and Sensing - Algorithms and Architectures, pp. 11\u201335. IGI Global (2012)","DOI":"10.4018\/978-1-61350-092-7.ch002"},{"key":"17_CR33","doi-asserted-by":"crossref","unstructured":"Tomforde, S., H\u00e4hner, J., Sick, B.: Interwoven systems. Inform. Spektrum 37(5), 483\u2013487 (2014)","DOI":"10.1007\/s00287-014-0827-z"},{"key":"17_CR34","doi-asserted-by":"publisher","unstructured":"Tomforde, S., Prothmann, H., Branke, J., et al.: Observation and Control of Organic Systems. In: M\u00fcller-Schloer, C., Schmeck, H., Ungerer, T. (eds.) Organic Computing \u2014 A Paradigm Shift for Complex Systems, pp. 325\u2013338. Springer, Cham (2011). https:\/\/doi.org\/10.1007\/978-3-0348-0130-0_21","DOI":"10.1007\/978-3-0348-0130-0_21"},{"key":"17_CR35","doi-asserted-by":"crossref","unstructured":"Tomforde, S., Sick, B., M\u00fcller-Schloer, C.: Organic computing in the spotlight. arXiv:1701.08125v1 [cs.MA] (2017)","DOI":"10.1007\/978-3-319-68477-2_1"},{"key":"17_CR36","doi-asserted-by":"crossref","unstructured":"von Mammen, S., Tomforde, S., H\u00f6hner, J., et al.: OCbotics: an organic computing approach to collaborative robotic swarms. In: 2014 IEEE Symposium on Swarm Intelligence, pp. 1\u20138 (2014)","DOI":"10.1109\/SIS.2014.7011781"},{"issue":"2","key":"17_CR37","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1162\/evco.1995.3.2.149","volume":"3","author":"SW Wilson","year":"1995","unstructured":"Wilson, S.W.: Classifier fitness based on accuracy. Evol. Comput. 3(2), 149\u2013175 (1995)","journal-title":"Evol. Comput."},{"key":"17_CR38","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1007\/3-540-45027-0_11","volume-title":"Learning Classifier Systems","author":"SW Wilson","year":"2000","unstructured":"Wilson, S.W.: Get real! XCS with continuous-valued inputs. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 1999. LNCS (LNAI), vol. 1813, pp. 209\u2013219. Springer, Heidelberg (2000). https:\/\/doi.org\/10.1007\/3-540-45027-0_11"},{"issue":"2","key":"17_CR39","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1023\/A:1016535925043","volume":"1","author":"SW Wilson","year":"2002","unstructured":"Wilson, S.W.: Classifiers that approximate functions. Nat. Comput. 1(2), 211\u2013234 (2002)","journal-title":"Nat. Comput."},{"issue":"7896","key":"17_CR40","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1038\/s41586-021-04357-7","volume":"602","author":"PR Wurman","year":"2022","unstructured":"Wurman, P.R., Barrett, S., Kawamoto, K., MacGlashan, J., Subramanian, K., Walsh, T.J., et al.: Outracing champion Gran Turismo drivers with deep reinforcement learning. Nature 602(7896), 223\u2013228 (2022)","journal-title":"Nature"}],"container-title":["Lecture Notes in Computer Science","Architecture of Computing Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21867-5_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T08:30:33Z","timestamp":1728549033000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21867-5_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031218668","9783031218675"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21867-5_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"14 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ARCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Architecture of Computing Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Heilbronn","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"35","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"arcs2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/arcs-conference.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,87","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}