{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T19:49:56Z","timestamp":1743018596054,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030616151"},{"type":"electronic","value":"9783030616168"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-61616-8_34","type":"book-chapter","created":{"date-parts":[[2020,10,16]],"date-time":"2020-10-16T23:07:42Z","timestamp":1602889662000},"page":"420-431","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Policy Entropy for Out-of-Distribution Classification"],"prefix":"10.1007","author":[{"given":"Andreas","family":"Sedlmeier","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"M\u00fcller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steffen","family":"Illium","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Claudia","family":"Linnhoff-Popien","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,14]]},"reference":[{"key":"34_CR1","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/978-3-319-14142-8_8","volume-title":"Data Mining","author":"CC Aggarwal","year":"2015","unstructured":"Aggarwal, C.C.: Outlier analysis. In: Aggarwal, C.C., et al. (eds.) Data Mining, pp. 237\u2013263. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-14142-8_8"},{"key":"34_CR2","unstructured":"Andrychowicz, O.M., Baker, et al.: Learning dexterous in-hand manipulation. Int. J. Robot. Res. 39(1), 3\u201320 (2020)"},{"key":"34_CR3","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1613\/jair.3912","volume":"47","author":"MG Bellemare","year":"2013","unstructured":"Bellemare, M.G., Naddaf, Y., Veness, J., Bowling, M.: The arcade learning environment: an evaluation platform for general agents. J. Artif. Intell. Res. 47, 253\u2013279 (2013)","journal-title":"J. Artif. Intell. Res."},{"key":"34_CR4","unstructured":"Berner, C., et al.: Dota 2 with large scale deep reinforcement learning. arXiv preprint arXiv:1912.06680 (2019)"},{"key":"34_CR5","unstructured":"Brockman, G., et al.: Openai gym (2016)"},{"key":"34_CR6","unstructured":"Cobbe, K., Hesse, C., Hilton, J., Schulman, J.: Leveraging procedural generation to benchmark reinforcement learning. arXiv preprint arXiv:1912.01588 (2019)"},{"key":"34_CR7","unstructured":"Dhariwal, P., et al.: Openai baselines (2017)"},{"key":"34_CR8","unstructured":"Espeholt, L., Soyer, H., Munos, R., et al.: IMPALA: scalable distributed deep-RL with importance weighted actor-learner architectures. CoRR (2018)"},{"key":"34_CR9","unstructured":"Farebrother, J., Machado, M.C., Bowling, M.: Generalization and regularization in DQN (2018)"},{"key":"34_CR10","unstructured":"Haarnoja, T., Tang, H., Abbeel, P., Levine, S.: Reinforcement learning with deep energy-based policies. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 1352\u20131361. JMLR. org (2017)"},{"key":"34_CR11","unstructured":"Hendrycks, D., Gimpel, K.: A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks. ArXiv e-prints, October 2016"},{"key":"34_CR12","doi-asserted-by":"crossref","unstructured":"Liang, S., Li, Y., Srikant, R.: Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks. ArXiv e-prints, June 2017","DOI":"10.2352\/ISSN.2470-1173.2017.7.MWSF-329"},{"key":"34_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: Generative adversarial active learning for unsupervised outlier detection. IEEE Trans. Knowl. Data Eng. (2019)","DOI":"10.1109\/TKDE.2019.2905606"},{"key":"34_CR14","unstructured":"Mnih, V., et al.: Asynchronous methods for deep reinforcement learning. CoRR (2016)"},{"key":"34_CR15","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.sigpro.2013.12.026","volume":"99","author":"MA Pimentel","year":"2014","unstructured":"Pimentel, M.A., Clifton, D.A., Clifton, L., Tarassenko, L.: A review of novelty detection. Sig. Process. 99, 215\u2013249 (2014)","journal-title":"Sig. Process."},{"key":"34_CR16","unstructured":"Puterman, M.L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, Hoboken (2014)"},{"key":"34_CR17","unstructured":"Schulman, J., Wolski, F., et al.: Proximal policy optimization algorithms. CoRR (2017)"},{"key":"34_CR18","doi-asserted-by":"crossref","unstructured":"Sedlmeier, A., Gabor, T., Phan, T., Belzner, L., Linnhoff-Popien, C.: Uncertainty-based out-of-distribution classification in deep reinforcement learning, pp. 522\u2013529 (2020)","DOI":"10.5220\/0008949905220529"},{"key":"34_CR19","unstructured":"Sutton, R.S., Barto, A.G.: Introduction to Reinforcement Learning, vol. 135. MIT Press, Cambridge (1998)"},{"issue":"3","key":"34_CR20","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1080\/09540099108946587","volume":"3","author":"RJ Williams","year":"1991","unstructured":"Williams, R.J., Peng, J.: Function optimization using connectionist reinforcement learning algorithms. Connection Sci. 3(3), 241\u2013268 (1991)","journal-title":"Connection Sci."},{"key":"34_CR21","unstructured":"Zhang, C., Vinyals, O., Munos, R., Bengio, S.: A study on overfitting in deep reinforcement learning (2018)"},{"issue":"96","key":"34_CR22","first-page":"1","volume":"20","author":"Y Zhao","year":"2019","unstructured":"Zhao, Y., Nasrullah, Z., Li, Z.: PyOD: a python toolbox for scalable outlier detection. J. Mach. Learn. Res. 20(96), 1\u20137 (2019)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-61616-8_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T22:03:36Z","timestamp":1619301816000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-61616-8_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030616151","9783030616168"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-61616-8_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"14 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bratislava","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovakia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"249","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":"139","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":"56% - 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","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":"2.5","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)"}},{"value":"*The conference was postponed to 2021 due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}