{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T00:52:52Z","timestamp":1759452772814,"version":"build-2065373602"},"reference-count":20,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Intell. Syst."],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1109\/mis.2024.3469574","type":"journal-article","created":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T14:40:10Z","timestamp":1727448010000},"page":"5-15","source":"Crossref","is-referenced-by-count":0,"title":["A Q-Learning Novelty Search Strategy for Evaluating Robustness of Deep Reinforcement Learning in Open-World Environments"],"prefix":"10.1109","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8524-2855","authenticated-orcid":false,"given":"Shafkat","family":"Islam","sequence":"first","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0637-7856","authenticated-orcid":false,"given":"Min-Hsueh","family":"Chiu","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5050-0220","authenticated-orcid":false,"given":"Trevor","family":"Bonjour","sequence":"additional","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4502-8557","authenticated-orcid":false,"given":"Ruy","family":"de Oliveira","sequence":"additional","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3803-8672","authenticated-orcid":false,"given":"Bharat","family":"Bhargava","sequence":"additional","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5988-8305","authenticated-orcid":false,"given":"Mayank","family":"Kejriwal","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, CA, USA"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i17.17766"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i17.17766"},{"volume-title":"17 fatalities, 736 crashes: The shocking toll of teslas autopilot","year":"2023","author":"Siddiqui","key":"ref3"},{"key":"ref4","article-title":"Designing artificial intelligence for open worlds","volume-title":"Proc. AAAI Spring Symp. Designing Artif. Intell. Open Worlds","author":"Kejriwal","year":"2022"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-021-05961-4"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.3390\/make4010013"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/qrs57517.2022.00084"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01096"},{"key":"ref9","article-title":"Fake it until you make it: Towards accurate near-distribution novelty detection","author":"Mirzaei","year":"2022","journal-title":"11th Int. Conf. Learn. Representations"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00027"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/tifs.2022.3163592"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpat.2021.102364"},{"key":"ref13","article-title":"Learning to play monopoly: A reinforcement learning approach","volume-title":"Proc. 50th Anniversary Conv. Soc. Study Artif. Intell. Simul. Behav.","author":"Bailis","year":"2014"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/tencon.2019.8929523"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/tetci.2022.3166555"},{"key":"ref16","first-page":"18343","article-title":"Minedojo: Building open-ended embodied agents with internet-scale knowledge","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Fan","year":"2022"},{"article-title":"Maestro: Open-ended environment design for multi-agent reinforcement learning","year":"2023","author":"Samvelyan","key":"ref17"},{"article-title":"Craftax: A lightning-fast benchmark for open-ended reinforcement learning","year":"2024","author":"Matthews","key":"ref18"},{"article-title":"Playing atari with deep reinforcement learning","year":"2013","author":"Mnih","key":"ref19"},{"key":"ref20","first-page":"470","article-title":"A dataset perspective on offline reinforcement learning","volume-title":"Proc. 1st Conf. Lifelong Learn. Agents","author":"Schweighofer","year":"2022"}],"container-title":["IEEE Intelligent Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/9670\/11187315\/10697140.pdf?arnumber=10697140","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T17:42:36Z","timestamp":1759426956000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10697140\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":20,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/mis.2024.3469574","relation":{},"ISSN":["1541-1672","1941-1294"],"issn-type":[{"type":"print","value":"1541-1672"},{"type":"electronic","value":"1941-1294"}],"subject":[],"published":{"date-parts":[[2025,9]]}}}