{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T09:40:40Z","timestamp":1775122840744,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":30,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,5,9]],"date-time":"2023-05-09T00:00:00Z","timestamp":1683590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000181","name":"Air Force Office of Scientific Research","doi-asserted-by":"publisher","award":["FA9550-22-1-0019"],"award-info":[{"award-number":["FA9550-22-1-0019"]}],"id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000181","name":"Air Force Office of Scientific Research","doi-asserted-by":"publisher","award":["FA9550-23-1-0135"],"award-info":[{"award-number":["FA9550-23-1-0135"]}],"id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014037","name":"National Defense Science and Engineering Graduate","doi-asserted-by":"publisher","award":["FA9550-21-F-0003"],"award-info":[{"award-number":["FA9550-21-F-0003"]}],"id":[{"id":"10.13039\/100014037","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["2028001"],"award-info":[{"award-number":["2028001"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,5,9]]},"DOI":"10.1145\/3576841.3585936","type":"proceedings-article","created":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T16:18:19Z","timestamp":1683217099000},"page":"110-119","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Self-Preserving Genetic Algorithms for Safe Learning in Discrete Action Spaces"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4906-2179","authenticated-orcid":false,"given":"Preston K.","family":"Robinette","sequence":"first","affiliation":[{"name":"Vanderbilt University, Nashville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7147-1964","authenticated-orcid":false,"given":"Nathaniel P.","family":"Hamilton","sequence":"additional","affiliation":[{"name":"Parallax Advanced Research, Beavercreek, OH, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8021-9923","authenticated-orcid":false,"given":"Taylor T.","family":"Johnson","sequence":"additional","affiliation":[{"name":"Vanderbilt University, Nashville, TN, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,5,9]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11797"},{"key":"e_1_3_2_1_2_1","volume-title":"Neuron-like adaptive elements that can solve difficult learning control problems","author":"Barto Andrew G","year":"1983","unstructured":"Andrew G Barto , Richard S Sutton , and Charles W Anderson . 1983. Neuron-like adaptive elements that can solve difficult learning control problems . IEEE transactions on systems, man, and cybernetics 5 ( 1983 ), 834--846. Andrew G Barto, Richard S Sutton, and Charles W Anderson. 1983. Neuron-like adaptive elements that can solve difficult learning control problems. IEEE transactions on systems, man, and cybernetics 5 (1983), 834--846."},{"key":"e_1_3_2_1_3_1","volume-title":"Openai gym. arXiv preprint arXiv:1606.01540","author":"Brockman Greg","year":"2016","unstructured":"Greg Brockman , Vicki Cheung , Ludwig Pettersson , Jonas Schneider , John Schulman , Jie Tang , and Wojciech Zaremba . 2016. Openai gym. arXiv preprint arXiv:1606.01540 ( 2016 ). Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. 2016. Openai gym. arXiv preprint arXiv:1606.01540 (2016)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-021-05961-4"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2018.8569635"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.5555\/2789272.2886795"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2017.08.1217"},{"key":"e_1_3_2_1_8_1","volume-title":"IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA'04.","volume":"5","author":"Hu Yanrong","year":"2004","unstructured":"Yanrong Hu and Simon X Yang . 2004 . A knowledge based genetic algorithm for path planning of a mobile robot . In IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA'04. 2004, Vol. 5 . IEEE, 4350--4355. Yanrong Hu and Simon X Yang. 2004. A knowledge based genetic algorithm for path planning of a mobile robot. In IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA'04. 2004, Vol. 5. IEEE, 4350--4355."},{"key":"e_1_3_2_1_9_1","volume-title":"A closer look at invalid action masking in policy gradient algorithms. arXiv preprint arXiv:2006.14171","author":"Huang Shengyi","year":"2020","unstructured":"Shengyi Huang and Santiago Onta\u00f1\u00f3n . 2020. A closer look at invalid action masking in policy gradient algorithms. arXiv preprint arXiv:2006.14171 ( 2020 ). Shengyi Huang and Santiago Onta\u00f1\u00f3n. 2020. A closer look at invalid action masking in policy gradient algorithms. arXiv preprint arXiv:2006.14171 (2020)."},{"key":"e_1_3_2_1_10_1","volume-title":"Or-gym: A reinforcement learning library for operations research problems. arXiv preprint arXiv:2008.06319","author":"Hubbs Christian D","year":"2020","unstructured":"Christian D Hubbs , Hector D Perez , Owais Sarwar , Nikolaos V Sahinidis , Ignacio E Grossmann , and John M Wassick . 2020 . Or-gym: A reinforcement learning library for operations research problems. arXiv preprint arXiv:2008.06319 (2020). Christian D Hubbs, Hector D Perez, Owais Sarwar, Nikolaos V Sahinidis, Ignacio E Grossmann, and John M Wassick. 2020. Or-gym: A reinforcement learning library for operations research problems. arXiv preprint arXiv:2008.06319 (2020)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2018.2856198"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3272021"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CoG47356.2020.9231687"},{"key":"e_1_3_2_1_14_1","volume-title":"Industrial applications of genetic algorithms","author":"Karr Charles","unstructured":"Charles Karr and L Michael Freeman . 1998. Industrial applications of genetic algorithms . Vol. 5 . CRC press . Charles Karr and L Michael Freeman. 1998. Industrial applications of genetic algorithms. Vol. 5. CRC press."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1177\/0278364913495721"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/DASC50938.2020.9256446"},{"key":"e_1_3_2_1_17_1","volume-title":"International Conference on Machine Learning. PMLR, 3053--3062","author":"Liang Eric","year":"2018","unstructured":"Eric Liang , Richard Liaw , Robert Nishihara , Philipp Moritz , Roy Fox , Ken Goldberg , Joseph Gonzalez , Michael Jordan , and Ion Stoica . 2018 . RLlib: Abstractions for distributed reinforcement learning . In International Conference on Machine Learning. PMLR, 3053--3062 . Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael Jordan, and Ion Stoica. 2018. RLlib: Abstractions for distributed reinforcement learning. In International Conference on Machine Learning. PMLR, 3053--3062."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICII.2019.00063"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2019.2916583"},{"key":"e_1_3_2_1_20_1","volume-title":"Kit Sang Tang, and Sam Kwong","author":"Man Kim F","year":"2012","unstructured":"Kim F Man , Kit Sang Tang, and Sam Kwong . 2012 . Genetic algorithms for control and signal processing. Springer Science & Business Media . Kim F Man, Kit Sang Tang, and Sam Kwong. 2012. Genetic algorithms for control and signal processing. Springer Science & Business Media."},{"key":"e_1_3_2_1_21_1","volume-title":"Rafidah Md Noor, Celimuge Wu, and Yeh-Ching Low.","author":"Rasheed Faizan","year":"2020","unstructured":"Faizan Rasheed , Kok-Lim Alvin Yau , Rafidah Md Noor, Celimuge Wu, and Yeh-Ching Low. 2020 . Deep Reinforcement Learning for Traffic Signal Control: A Review. IEEE Access ( 2020). Faizan Rasheed, Kok-Lim Alvin Yau, Rafidah Md Noor, Celimuge Wu, and Yeh-Ching Low. 2020. Deep Reinforcement Learning for Traffic Signal Control: A Review. IEEE Access (2020)."},{"key":"e_1_3_2_1_23_1","volume-title":"Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347","author":"Schulman John","year":"2017","unstructured":"John Schulman , Filip Wolski , Prafulla Dhariwal , Alec Radford , and Oleg Klimov . 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 ( 2017 ). John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)."},{"key":"e_1_3_2_1_24_1","volume-title":"Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al.","author":"Silver David","year":"2016","unstructured":"David Silver , Aja Huang , Chris J Maddison , Arthur Guez , Laurent Sifre , George Van Den Driessche , Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al. 2016 . Mastering the game of Go with deep neural networks and tree search. nature 529, 7587 (2016), 484--489. David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al. 2016. Mastering the game of Go with deep neural networks and tree search. nature 529, 7587 (2016), 484--489."},{"key":"e_1_3_2_1_25_1","volume-title":"Operant behavior. American psychologist 18, 8","author":"Skinner Burrhus F","year":"1963","unstructured":"Burrhus F Skinner . 1963. Operant behavior. American psychologist 18, 8 ( 1963 ), 503. Burrhus F Skinner. 1963. Operant behavior. American psychologist 18, 8 (1963), 503."},{"key":"e_1_3_2_1_26_1","volume-title":"Deep neuroevolution: Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning. arXiv preprint arXiv:1712.06567","author":"Such Felipe Petroski","year":"2017","unstructured":"Felipe Petroski Such , Vashisht Madhavan , Edoardo Conti , Joel Lehman , Kenneth O Stanley , and Jeff Clune . 2017. Deep neuroevolution: Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning. arXiv preprint arXiv:1712.06567 ( 2017 ). Felipe Petroski Such, Vashisht Madhavan, Edoardo Conti, Joel Lehman, Kenneth O Stanley, and Jeff Clune. 2017. Deep neuroevolution: Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning. arXiv preprint arXiv:1712.06567 (2017)."},{"key":"e_1_3_2_1_27_1","volume-title":"Reinforcement learning: An introduction","author":"Sutton Richard S","unstructured":"Richard S Sutton and Andrew G Barto . 2018. Reinforcement learning: An introduction . MIT press . Richard S Sutton and Andrew G Barto. 2018. Reinforcement learning: An introduction. MIT press."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mechatronics.2003.10.001"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","DOI":"10.1109\/ROBOT.2003.1241759","volume-title":"2003 IEEE International Conference on Robotics and Automation (Cat. No. 03CH37422)","volume":"1","author":"Tu Jianping","year":"2003","unstructured":"Jianping Tu and Simon X Yang . 2003 . Genetic algorithm based path planning for a mobile robot . In 2003 IEEE International Conference on Robotics and Automation (Cat. No. 03CH37422) , Vol. 1 . IEEE, 1221--1226. Jianping Tu and Simon X Yang. 2003. Genetic algorithm based path planning for a mobile robot. In 2003 IEEE International Conference on Robotics and Automation (Cat. No. 03CH37422), Vol. 1. IEEE, 1221--1226."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Oriol Vinyals Igor Babuschkin Wojciech M Czarnecki Micha\u00ebl Mathieu Andrew Dudzik Junyoung Chung David H Choi Richard Powell Timo Ewalds Petko Georgiev etal 2019. Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature 575 7782 (2019) 350--354.  Oriol Vinyals Igor Babuschkin Wojciech M Czarnecki Micha\u00ebl Mathieu Andrew Dudzik Junyoung Chung David H Choi Richard Powell Timo Ewalds Petko Georgiev et al. 2019. Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature 575 7782 (2019) 350--354.","DOI":"10.1038\/s41586-019-1724-z"},{"key":"e_1_3_2_1_31_1","volume-title":"Optimization of molecules via deep reinforcement learning. Scientific reports 9, 1","author":"Zhou Zhenpeng","year":"2019","unstructured":"Zhenpeng Zhou , Steven Kearnes , Li Li , Richard N Zare , and Patrick Riley . 2019. Optimization of molecules via deep reinforcement learning. Scientific reports 9, 1 ( 2019 ), 1--10. Zhenpeng Zhou, Steven Kearnes, Li Li, Richard N Zare, and Patrick Riley. 2019. Optimization of molecules via deep reinforcement learning. Scientific reports 9, 1 (2019), 1--10."}],"event":{"name":"ICCPS '23: ACM\/IEEE 14th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2023)","location":"San Antonio TX USA","acronym":"ICCPS '23","sponsor":["SIGBED ACM Special Interest Group on Embedded Systems","IEEE TCRTS"]},"container-title":["Proceedings of the ACM\/IEEE 14th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2023)"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3576841.3585936","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3576841.3585936","content-type":"text\/html","content-version":"vor","intended-application":"syndication"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:47:27Z","timestamp":1750178847000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3576841.3585936"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,9]]},"references-count":30,"alternative-id":["10.1145\/3576841.3585936","10.1145\/3576841"],"URL":"https:\/\/doi.org\/10.1145\/3576841.3585936","relation":{},"subject":[],"published":{"date-parts":[[2023,5,9]]},"assertion":[{"value":"2023-05-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}