{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T13:13:54Z","timestamp":1685711634754},"publisher-location":"New York, NY, USA","reference-count":50,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,5,19]]},"DOI":"10.1145\/3447928.3456653","type":"proceedings-article","created":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T03:54:48Z","timestamp":1620100488000},"update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Verifiably safe exploration for end-to-end reinforcement learning"],"prefix":"10.1145","author":[{"given":"Nathan","family":"Hunt","sequence":"first","affiliation":[{"name":"Massachusetts Institute of Technology"}]},{"given":"Nathan","family":"Fulton","sequence":"additional","affiliation":[{"name":"MIT-IBM Watson AI Lab"}]},{"given":"Sara","family":"Magliacane","sequence":"additional","affiliation":[{"name":"University of Amsterdam"}]},{"given":"Trong Nghia","family":"Hoang","sequence":"additional","affiliation":[{"name":"MIT-IBM Watson AI Lab"}]},{"given":"Subhro","family":"Das","sequence":"additional","affiliation":[{"name":"MIT-IBM Watson AI Lab"}]},{"given":"Armando","family":"Solar-Lezama","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}]}],"member":"320","published-online":{"date-parts":[[2021,5,19]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Constrained Policy Optimization. In International Conference on Machine Learning (ICML 2017)","volume":"31","author":"Achiam Joshua","year":"2017","unstructured":"Joshua Achiam , David Held , Aviv Tamar , and Pieter Abbeel . 2017 . Constrained Policy Optimization. In International Conference on Machine Learning (ICML 2017) (Proceedings of Machine Learning Research , Vol. 70), Doina Precup and Yee Whye Teh (Eds.). PMLR, 22-- 31 . Joshua Achiam, David Held, Aviv Tamar, and Pieter Abbeel. 2017. Constrained Policy Optimization. In International Conference on Machine Learning (ICML 2017) (Proceedings of Machine Learning Research, Vol. 70), Doina Precup and Yee Whye Teh (Eds.). PMLR, 22--31."},{"key":"e_1_3_2_1_2_1","volume-title":"AAAI Conference on Artificial Intelligence.","author":"Alshiekh Mohammed","year":"2018","unstructured":"Mohammed Alshiekh , Roderick Bloem , R\u00fcdiger Ehlers , Bettina K\u00f6nighofer , Scott Niekum , and Ufuk Topcu . 2018 . Safe Reinforcement Learning via Shielding . In AAAI Conference on Artificial Intelligence. Mohammed Alshiekh, Roderick Bloem, R\u00fcdiger Ehlers, Bettina K\u00f6nighofer, Scott Niekum, and Ufuk Topcu. 2018. Safe Reinforcement Learning via Shielding. In AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_1_3_1","unstructured":"Felix Berkenkamp Matteo Turchetta Angela Schoellig and Andreas Krause. 2017. Safe model-based reinforcement learning with stability guarantees. In Advances in neural information processing systems. 908--918. Felix Berkenkamp Matteo Turchetta Angela Schoellig and Andreas Krause. 2017. Safe model-based reinforcement learning with stability guarantees. In Advances in neural information processing systems. 908--918."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013387"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Edmund M. Clarke Thomas A. Henzinger Helmut Veith and Roderick Bloem (Eds.). 2018. Handbook of Model Checking. Springer. Edmund M. Clarke Thomas A. Henzinger Helmut Veith and Roderick Bloem (Eds.). 2018. Handbook of Model Checking. Springer.","DOI":"10.1007\/978-3-319-10575-8"},{"key":"e_1_3_2_1_6_1","volume-title":"Safe exploration in continuous action spaces. arXiv preprint arXiv:1801.08757","author":"Dalal Gal","year":"2018","unstructured":"Gal Dalal , Krishnamurthy Dvijotham , Matej Vecerik , Todd Hester , Cosmin Paduraru , and Yuval Tassa . 2018. Safe exploration in continuous action spaces. arXiv preprint arXiv:1801.08757 ( 2018 ). Gal Dalal, Krishnamurthy Dvijotham, Matej Vecerik, Todd Hester, Cosmin Paduraru, and Yuval Tassa. 2018. Safe exploration in continuous action spaces. arXiv preprint arXiv:1801.08757 (2018)."},{"key":"e_1_3_2_1_7_1","volume-title":"Foundations for Restraining Bolts: Reinforcement Learning with LTLf\/LDLf Restraining Specifications. In International Conference on Automated Planning and Scheduling (ICAPS","author":"Giacomo Giuseppe De","year":"2019","unstructured":"Giuseppe De Giacomo , Luca Iocchi , Marco Favorito , and Fabio Patrizi . 2019 . Foundations for Restraining Bolts: Reinforcement Learning with LTLf\/LDLf Restraining Specifications. In International Conference on Automated Planning and Scheduling (ICAPS 2019). Giuseppe De Giacomo, Luca Iocchi, Marco Favorito, and Fabio Patrizi. 2019. Foundations for Restraining Bolts: Reinforcement Learning with LTLf\/LDLf Restraining Specifications. In International Conference on Automated Planning and Scheduling (ICAPS 2019)."},{"key":"e_1_3_2_1_8_1","volume-title":"Bellerophon: Tactical Theorem Proving for Hybrid Systems. In International Conference on Interactive Theorem Proving.","author":"Fulton Nathan","year":"2017","unstructured":"Nathan Fulton , Stefan Mitsch , Brandon Bohrer , and Andr\u00e9 Platzer . 2017 . Bellerophon: Tactical Theorem Proving for Hybrid Systems. In International Conference on Interactive Theorem Proving. Nathan Fulton, Stefan Mitsch, Brandon Bohrer, and Andr\u00e9 Platzer. 2017. Bellerophon: Tactical Theorem Proving for Hybrid Systems. In International Conference on Interactive Theorem Proving."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Nathan Fulton Stefan Mitsch Jan-David Quesel Marcus V\u00f6lp and Andr\u00e9 Platzer. 2015. KeYmaera X: An Axiomatic Tactical Theorem Prover for Hybrid Systems. In CADE. Nathan Fulton Stefan Mitsch Jan-David Quesel Marcus V\u00f6lp and Andr\u00e9 Platzer. 2015. KeYmaera X: An Axiomatic Tactical Theorem Prover for Hybrid Systems. In CADE.","DOI":"10.1007\/978-3-319-21401-6_36"},{"key":"e_1_3_2_1_10_1","volume-title":"AAAI Conference on Artificial Intelligence.","author":"Fulton Nathan","year":"2018","unstructured":"Nathan Fulton and Andr\u00e9 Platzer . 2018 . Safe Reinforcement Learning via Formal Methods: Toward Safe Control Through Proof and Learning . In AAAI Conference on Artificial Intelligence. Nathan Fulton and Andr\u00e9 Platzer. 2018. Safe Reinforcement Learning via Formal Methods: Toward Safe Control Through Proof and Learning. In AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-17462-0_28"},{"key":"e_1_3_2_1_12_1","volume-title":"A comprehensive survey on safe reinforcement learning. Journal of Machine Learning Research","author":"Garc\u0131a Javier","year":"2015","unstructured":"Javier Garc\u0131a and Fernando Fern\u00e1ndez . 2015. A comprehensive survey on safe reinforcement learning. Journal of Machine Learning Research ( 2015 ). Javier Garc\u0131a and Fernando Fern\u00e1ndez. 2015. A comprehensive survey on safe reinforcement learning. Journal of Machine Learning Research (2015)."},{"key":"e_1_3_2_1_13_1","volume-title":"Towards deep symbolic reinforcement learning. arXiv preprint arXiv:1609.05518","author":"Garnelo Marta","year":"2016","unstructured":"Marta Garnelo , Kai Arulkumaran , and Murray Shanahan . 2016. Towards deep symbolic reinforcement learning. arXiv preprint arXiv:1609.05518 ( 2016 ). Marta Garnelo, Kai Arulkumaran, and Murray Shanahan. 2016. Towards deep symbolic reinforcement learning. arXiv preprint arXiv:1609.05518 (2016)."},{"key":"e_1_3_2_1_14_1","unstructured":"Vikash Goel Jameson Weng and Pascal Poupart. 2018. Unsupervised video object segmentation for deep reinforcement learning. In Advances in Neural Information Processing Systems. Vikash Goel Jameson Weng and Pascal Poupart. 2018. Unsupervised video object segmentation for deep reinforcement learning. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_15_1","volume-title":"Omega-Regular Objectives in Model-Free Reinforcement Learning. In TACAS","author":"Hahn Ernst Moritz","year":"2019","unstructured":"Ernst Moritz Hahn , Mateo Perez , Sven Schewe , Fabio Somenzi , Ashutosh Trivedi , and Dominik Wojtczak . 2019 . Omega-Regular Objectives in Model-Free Reinforcement Learning. In TACAS 2019. Ernst Moritz Hahn, Mateo Perez, Sven Schewe, Fabio Somenzi, Ashutosh Trivedi, and Dominik Wojtczak. 2019. Omega-Regular Objectives in Model-Free Reinforcement Learning. In TACAS 2019."},{"key":"e_1_3_2_1_16_1","volume-title":"Logically-constrained reinforcement learning. arXiv preprint arXiv:1801.08099","author":"Hasanbeig Mohammadhosein","year":"2018","unstructured":"Mohammadhosein Hasanbeig , Alessandro Abate , and Daniel Kroening . 2018. Logically-constrained reinforcement learning. arXiv preprint arXiv:1801.08099 ( 2018 ). Mohammadhosein Hasanbeig, Alessandro Abate, and Daniel Kroening. 2018. Logically-constrained reinforcement learning. arXiv preprint arXiv:1801.08099 (2018)."},{"key":"e_1_3_2_1_17_1","volume-title":"Logically-Correct Reinforcement Learning. CoRR abs\/1801.08099","author":"Hasanbeig Mohammadhosein","year":"2018","unstructured":"Mohammadhosein Hasanbeig , Alessandro Abate , and Daniel Kroening . 2018. Logically-Correct Reinforcement Learning. CoRR abs\/1801.08099 ( 2018 ). arXiv:1801.08099 Mohammadhosein Hasanbeig, Alessandro Abate, and Daniel Kroening. 2018. Logically-Correct Reinforcement Learning. CoRR abs\/1801.08099 (2018). arXiv:1801.08099"},{"key":"e_1_3_2_1_18_1","volume-title":"Certified Reinforcement Learning with Logic Guidance. CoRR abs\/1902.00778","author":"Hasanbeig Mohammadhosein","year":"2019","unstructured":"Mohammadhosein Hasanbeig , Alessandro Abate , and Daniel Kroening . 2019. Certified Reinforcement Learning with Logic Guidance. CoRR abs\/1902.00778 ( 2019 ). arXiv:1902.00778 http:\/\/arxiv.org\/abs\/1902.00778 Mohammadhosein Hasanbeig, Alessandro Abate, and Daniel Kroening. 2019. Certified Reinforcement Learning with Logic Guidance. CoRR abs\/1902.00778 (2019). arXiv:1902.00778 http:\/\/arxiv.org\/abs\/1902.00778"},{"key":"e_1_3_2_1_19_1","volume-title":"Cautious reinforcement learning with logical constraints. arXiv preprint arXiv:2002.12156","author":"Hasanbeig Mohammadhosein","year":"2020","unstructured":"Mohammadhosein Hasanbeig , Alessandro Abate , and Daniel Kroening . 2020. Cautious reinforcement learning with logical constraints. arXiv preprint arXiv:2002.12156 ( 2020 ). Mohammadhosein Hasanbeig, Alessandro Abate, and Daniel Kroening. 2020. Cautious reinforcement learning with logical constraints. arXiv preprint arXiv:2002.12156 (2020)."},{"key":"e_1_3_2_1_20_1","volume-title":"Daniel Kroening, George J. Pappas, and Insup Lee.","author":"Hasanbeig Mohammadhosein","year":"2019","unstructured":"Mohammadhosein Hasanbeig , Yiannis Kantaros , Alessand ro Abate , Daniel Kroening, George J. Pappas, and Insup Lee. 2019 . Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction Guarantees. arXiv e-prints, Article arXiv:1909.05304 (Sept. 2019), arXiv:1909.05304 pages. arXiv:1909.05304 [cs.LO] Mohammadhosein Hasanbeig, Yiannis Kantaros, Alessand ro Abate, Daniel Kroening, George J. Pappas, and Insup Lee. 2019. Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction Guarantees. arXiv e-prints, Article arXiv:1909.05304 (Sept. 2019), arXiv:1909.05304 pages. arXiv:1909.05304 [cs.LO]"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Nathan Hunt Nathan Fulton Sara Magliacane Nghia Hoang Subhro Das and Armando Solar-Lezama. 2020. Verifiably Safe Exploration for End-to-End Reinforcement Learning. arXiv:2007.01223 [cs.AI] Nathan Hunt Nathan Fulton Sara Magliacane Nghia Hoang Subhro Das and Armando Solar-Lezama. 2020. Verifiably Safe Exploration for End-to-End Reinforcement Learning. arXiv:2007.01223 [cs.AI]","DOI":"10.1145\/3447928.3456653"},{"key":"e_1_3_2_1_23_1","unstructured":"ISO-26262. 2011. International Organization for Standardization 26262 Road vehicles - Functional safety. (2011). ISO-26262. 2011. International Organization for Standardization 26262 Road vehicles - Functional safety. (2011)."},{"key":"e_1_3_2_1_24_1","volume-title":"Paddock","author":"Kalra Nidhi","year":"2016","unstructured":"Nidhi Kalra and Susan M . Paddock . 2016 . Driving to Safety : How Many Miles of Driving Would It Take to Demonstrate Autonomous Vehicle Reliability? RAND Corporation . Nidhi Kalra and Susan M. Paddock. 2016. Driving to Safety: How Many Miles of Driving Would It Take to Demonstrate Autonomous Vehicle Reliability? RAND Corporation."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2018.8619572"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"e_1_3_2_1_27_1","volume-title":"Object-sensitive deep reinforcement learning. arXiv preprint arXiv:1809.06064","author":"Li Yuezhang","year":"2018","unstructured":"Yuezhang Li , Katia Sycara , and Rahul Iyer . 2018. Object-sensitive deep reinforcement learning. arXiv preprint arXiv:1809.06064 ( 2018 ). Yuezhang Li, Katia Sycara, and Rahul Iyer. 2018. Object-sensitive deep reinforcement learning. arXiv preprint arXiv:1809.06064 (2018)."},{"key":"e_1_3_2_1_28_1","volume-title":"Task-Relevant Object Discovery and Categorization for Playing First-person Shooter Games. arXiv preprint arXiv:1806.06392","author":"Liang Junchi","year":"2018","unstructured":"Junchi Liang and Abdeslam Boularias . 2018. Task-Relevant Object Discovery and Categorization for Playing First-person Shooter Games. arXiv preprint arXiv:1806.06392 ( 2018 ). Junchi Liang and Abdeslam Boularias. 2018. Task-Relevant Object Discovery and Categorization for Playing First-person Shooter Games. arXiv preprint arXiv:1806.06392 (2018)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"e_1_3_2_1_30_1","volume-title":"Robot representing and reasoning with knowledge from reinforcement learning. arXiv preprint arXiv:1809.11074","author":"Lu Keting","year":"2018","unstructured":"Keting Lu , Shiqi Zhang , Peter Stone , and Xiaoping Chen . 2018. Robot representing and reasoning with knowledge from reinforcement learning. arXiv preprint arXiv:1809.11074 ( 2018 ). Keting Lu, Shiqi Zhang, Peter Stone, and Xiaoping Chen. 2018. Robot representing and reasoning with knowledge from reinforcement learning. arXiv preprint arXiv:1809.11074 (2018)."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33012970"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Stefan Mitsch Khalil Ghorbal and Andr\u00e9 Platzer. 2013. On Provably Safe Obstacle Avoidance for Autonomous Robotic Ground Vehicles. In Robotics: Science and Systems Paul Newman Dieter Fox and David Hsu (Eds.). Stefan Mitsch Khalil Ghorbal and Andr\u00e9 Platzer. 2013. On Provably Safe Obstacle Avoidance for Autonomous Robotic Ground Vehicles. In Robotics: Science and Systems Paul Newman Dieter Fox and David Hsu (Eds.).","DOI":"10.15607\/RSS.2013.IX.014"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10703-016-0241-z"},{"key":"e_1_3_2_1_34_1","volume-title":"Playing Atari With Deep Reinforcement Learning. In NIPS Deep Learning Workshop.","author":"Mnih Volodymyr","year":"2013","unstructured":"Volodymyr Mnih , Koray Kavukcuoglu , David Silver , Alex Graves , Ioannis Antonoglou , Daan Wierstra , and Martin Riedmiller . 2013 . Playing Atari With Deep Reinforcement Learning. In NIPS Deep Learning Workshop. Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. 2013. Playing Atari With Deep Reinforcement Learning. In NIPS Deep Learning Workshop."},{"key":"e_1_3_2_1_35_1","volume-title":"Stoller","author":"Phan Dung","year":"2019","unstructured":"Dung Phan , Nicola Paoletti , Radu Grosu , Nils Jansen , Scott A. Smolka , and Scott D . Stoller . 2019 . Neural Simplex Architecture . (2019). Dung Phan, Nicola Paoletti, Radu Grosu, Nils Jansen, Scott A. Smolka, and Scott D. Stoller. 2019. Neural Simplex Architecture. (2019)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10817-008-9103-8"},{"key":"e_1_3_2_1_37_1","volume-title":"Logical Analysis of Hybrid Systems: Proving Theorems for Complex Dynamics","author":"Platzer Andr\u00e9","unstructured":"Andr\u00e9 Platzer . 2010. Logical Analysis of Hybrid Systems: Proving Theorems for Complex Dynamics . Springer , Heidelberg . Andr\u00e9 Platzer. 2010. Logical Analysis of Hybrid Systems: Proving Theorems for Complex Dynamics. Springer, Heidelberg."},{"key":"e_1_3_2_1_38_1","volume-title":"Logics of Dynamical Systems","author":"Platzer Andr\u00e9","unstructured":"Andr\u00e9 Platzer . 2012. Logics of Dynamical Systems . In LICS. IEEE , 13--24. Andr\u00e9 Platzer. 2012. Logics of Dynamical Systems. In LICS. IEEE, 13--24."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Andr\u00e9 Platzer. 2015. A Uniform Substitution Calculus for Differential Dynamic Logic. In CADE. Andr\u00e9 Platzer. 2015. A Uniform Substitution Calculus for Differential Dynamic Logic. In CADE.","DOI":"10.1007\/978-3-319-21401-6_32"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10817-016-9385-1"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-71493-4_37"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10009-015-0367-0"},{"key":"e_1_3_2_1_43_1","unstructured":"Alex Ray Joshua Achiam and Dario Amodei. 2019. Benchmarking Safe Exploration in Deep Reinforcement Learning. (2019). Alex Ray Joshua Achiam and Dario Amodei. 2019. Benchmarking Safe Exploration in Deep Reinforcement Learning. (2019)."},{"key":"e_1_3_2_1_44_1","volume-title":"Proceedings of the 32nd International Conference on Machine Learning (ICML 2015) (JMLR Workshop and Conference Proceedings","volume":"1897","author":"Schulman John","year":"2015","unstructured":"John Schulman , Sergey Levine , Pieter Abbeel , Michael I. Jordan , and Philipp Moritz . 2015 . Trust Region Policy Optimization . In Proceedings of the 32nd International Conference on Machine Learning (ICML 2015) (JMLR Workshop and Conference Proceedings , Vol. 37), Francis R. Bach and David M. Blei (Eds.). 1889-- 1897 . John Schulman, Sergey Levine, Pieter Abbeel, Michael I. Jordan, and Philipp Moritz. 2015. Trust Region Policy Optimization. In Proceedings of the 32nd International Conference on Machine Learning (ICML 2015) (JMLR Workshop and Conference Proceedings, Vol. 37), Francis R. Bach and David M. Blei (Eds.). 1889--1897."},{"key":"e_1_3_2_1_45_1","unstructured":"John Schulman Filip Wolski Prafulla Dhariwal Alec Radford and Oleg Klimov. 2017. Proximal Policy Optimization Algorithms. (2017). arXiv:1707.06347 http:\/\/arxiv.org\/abs\/1707.06347 John Schulman Filip Wolski Prafulla Dhariwal Alec Radford and Oleg Klimov. 2017. Proximal Policy Optimization Algorithms. (2017). arXiv:1707.06347 http:\/\/arxiv.org\/abs\/1707.06347"},{"key":"e_1_3_2_1_46_1","volume-title":"Barto","author":"Sutton Richard S.","year":"1998","unstructured":"Richard S. Sutton and Andrew G . Barto . 1998 . Reinforcement Learning : An Introduction. MIT Press , Cambridge, MA. Richard S. Sutton and Andrew G. Barto. 1998. Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA."},{"key":"e_1_3_2_1_47_1","volume-title":"Nathaniel Hamilton, Xiaodong Yang, Joel Rosenfeld, and Taylor T Johnson.","author":"Xiang Weiming","year":"2018","unstructured":"Weiming Xiang , Patrick Musau , Ayana A Wild , Diego Manzanas Lopez , Nathaniel Hamilton, Xiaodong Yang, Joel Rosenfeld, and Taylor T Johnson. 2018 . Verification for machine learning, autonomy, and neural networks survey. arXiv (2018). Weiming Xiang, Patrick Musau, Ayana A Wild, Diego Manzanas Lopez, Nathaniel Hamilton, Xiaodong Yang, Joel Rosenfeld, and Taylor T Johnson. 2018. Verification for machine learning, autonomy, and neural networks survey. arXiv (2018)."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"crossref","unstructured":"Fangkai Yang Steven Gustafson Alexander Elkholy Daoming Lyu and Bo Liu. 2019. Program Search for Machine Learning Pipelines Leveraging Symbolic Planning and Reinforcement Learning. In Genetic Programming Theory and Practice XVI. Fangkai Yang Steven Gustafson Alexander Elkholy Daoming Lyu and Bo Liu. 2019. Program Search for Machine Learning Pipelines Leveraging Symbolic Planning and Reinforcement Learning. In Genetic Programming Theory and Practice XVI.","DOI":"10.1007\/978-3-030-04735-1_11"},{"key":"e_1_3_2_1_49_1","volume-title":"Peorl: Integrating symbolic planning and hierarchical reinforcement learning for robust decision-making. arXiv preprint arXiv:1804.07779","author":"Yang Fangkai","year":"2018","unstructured":"Fangkai Yang , Daoming Lyu , Bo Liu , and Steven Gustafson . 2018 . Peorl: Integrating symbolic planning and hierarchical reinforcement learning for robust decision-making. arXiv preprint arXiv:1804.07779 (2018). Fangkai Yang, Daoming Lyu, Bo Liu, and Steven Gustafson. 2018. Peorl: Integrating symbolic planning and hierarchical reinforcement learning for robust decision-making. arXiv preprint arXiv:1804.07779 (2018)."},{"key":"e_1_3_2_1_50_1","volume-title":"Objects as Points. arXiv preprint arXiv:1904.07850","author":"Zhou Xingyi","year":"2019","unstructured":"Xingyi Zhou , Dequan Wang , and Philipp Kr\u00e4henb\u00fchl . 2019. Objects as Points. arXiv preprint arXiv:1904.07850 ( 2019 ). Xingyi Zhou, Dequan Wang, and Philipp Kr\u00e4henb\u00fchl. 2019. Objects as Points. arXiv preprint arXiv:1904.07850 (2019)."}],"event":{"name":"HSCC '21: 24th ACM International Conference on Hybrid Systems: Computation and Control","location":"Nashville Tennessee","acronym":"HSCC '21","sponsor":["SIGBED ACM Special Interest Group on Embedded Systems"]},"container-title":["Proceedings of the 24th International Conference on Hybrid Systems: Computation and Control"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447928.3456653","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T19:55:51Z","timestamp":1673466951000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447928.3456653"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,19]]},"references-count":50,"alternative-id":["10.1145\/3447928.3456653","10.1145\/3447928"],"URL":"http:\/\/dx.doi.org\/10.1145\/3447928.3456653","relation":{},"published":{"date-parts":[[2021,5,19]]},"assertion":[{"value":"2021-05-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}