{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T05:58:28Z","timestamp":1726034308772},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030243364"},{"type":"electronic","value":"9783030243371"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","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":[[2019]]},"DOI":"10.1007\/978-3-030-24337-1_8","type":"book-chapter","created":{"date-parts":[[2019,6,28]],"date-time":"2019-06-28T10:03:08Z","timestamp":1561716188000},"page":"147-163","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["What\u2019s in a Game? The Effect of Game Complexity on Deep Reinforcement Learning"],"prefix":"10.1007","author":[{"given":"Erdem","family":"Emekligil","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ethem","family":"Alpayd\u0131n","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,6,29]]},"reference":[{"key":"8_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1007\/978-3-319-77538-8_26","volume-title":"Applications of Evolutionary Computation","author":"D Anderson","year":"2018","unstructured":"Anderson, D., Stephenson, M., Togelius, J., Salge, C., Levine, J., Renz, J.: Deceptive games. In: Sim, K., Kaufmann, P. (eds.) EvoApplications 2018. LNCS, vol. 10784, pp. 376\u2013391. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-77538-8_26"},{"key":"8_CR2","unstructured":"Bellemare, M.G., Dabney, W., Munos, R.: A distributional perspective on reinforcement learning. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 449\u2013458. JMLR. org. (2017)"},{"key":"8_CR3","unstructured":"Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, AAAIWS 1994, pp. 359\u2013370. AAAI Press (1994)"},{"key":"8_CR4","volume-title":"Characteristics of Games","author":"GS Elias","year":"2012","unstructured":"Elias, G.S., Garfield, R., Gutschera, K.R.: Characteristics of Games. The MIT Press, Cambridge (2012)"},{"key":"8_CR5","unstructured":"Hasselt, H.V., Guez, A., Silver, D.: Deep reinforcement learning with double q-learning. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI 2016, pp. 2094\u20132100. AAAI Press (2016)"},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Hessel, M., Modayil, J., van Hasselt, H., et al.: Rainbow: combining improvements in deep reinforcement learning. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v33i01.33013796"},{"issue":"39","key":"8_CR7","first-page":"1","volume":"17","author":"S Levine","year":"2016","unstructured":"Levine, S., Finn, C., Darrell, T., et al.: End-to-end training of deep visuomotor policies. J. Mach. Learn. Res. 17(39), 1\u201340 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"8_CR8","unstructured":"Lillicrap, T.P., Hunt, J.J., Pritzel, A., et al.: Continuous control with deep reinforcement learning. ArXiv e-prints, September 2015"},{"key":"8_CR9","unstructured":"Mnih, V., Badia, A.P., Mirza, M., et al.: Asynchronous methods for deep reinforcement learning. In: Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, 19\u201324 June 2016, pp. 1928\u20131937 (2016)"},{"issue":"7540","key":"8_CR10","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih, V., Kavukcuoglu, K., Silver, D., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529\u2013533 (2015)","journal-title":"Nature"},{"key":"8_CR11","unstructured":"Nair, A., Srinivasan, P., Blackwell, S., et al.: Massively parallel methods for deep reinforcement learning. ArXiv e-prints, July 2015"},{"issue":"4","key":"8_CR12","doi-asserted-by":"publisher","first-page":"81:1","DOI":"10.1145\/2897824.2925881","volume":"35","author":"XB Peng","year":"2016","unstructured":"Peng, X.B., Berseth, G., van de Panne, M.: Terrain-adaptive locomotion skills using deep reinforcement learning. ACM Trans. Graph. 35(4), 81:1\u201381:12 (2016). https:\/\/doi.org\/10.1145\/2897824.2925881","journal-title":"ACM Trans. Graph."},{"key":"8_CR13","unstructured":"Schaul, T., Quan, J., Antonoglou, I., et al.: Prioritized experience replay. ArXiv e-prints, November 2015"},{"issue":"7587","key":"8_CR14","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1038\/nature16961","volume":"529","author":"D Silver","year":"2016","unstructured":"Silver, D., Huang, A., Maddison, C.J., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484\u2013489 (2016)","journal-title":"Nature"},{"key":"8_CR15","unstructured":"Silver, D., Hubert, T., Schrittwieser, J., et al.: Mastering Chess and Shogi by self-play with a general reinforcement learning algorithm. ArXiv e-prints, December 2017"},{"key":"8_CR16","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1038\/nature24270","volume":"550","author":"D Silver","year":"2017","unstructured":"Silver, D., Schrittwieser, J., Simonyan, K., et al.: Mastering the game of go without human knowledge. Nature 550, 354\u2013359 (2017)","journal-title":"Nature"},{"key":"8_CR17","unstructured":"Wang, Z., Schaul, T., Hessel, M., et al.: Dueling network architectures for deep reinforcement learning. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning, ICML 2016, vol. 48, pp. 1995\u20132003. JMLR.org (2016)"},{"key":"8_CR18","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-319-63519-4_1","volume-title":"Artificial Intelligence and Games","author":"Georgios N. Yannakakis","year":"2018","unstructured":"Yannakakis, G.N., Togelius, J.: Artificial Intelligence and Games. Springer, Heidelberg (2018). http:\/\/gameaibook.org"}],"container-title":["Communications in Computer and Information Science","Computer Games"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-24337-1_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T09:44:47Z","timestamp":1575193487000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-24337-1_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030243364","9783030243371"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-24337-1_8","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"29 June 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CGW","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Workshop on Computer Games","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Stockholm","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sweden","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 July 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 July 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cgw2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.lamsade.dauphine.fr\/~cazenave\/cgw2018\/cgw2018.html","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":"no","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"15","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":"8","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":"53% - 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","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}