{"doi":"10.1007/978-3-031-30823-9_1","project":[{"call_identifier":"H2020","_id":"0599E47C-7A3F-11EA-A408-12923DDC885E","grant_number":"863818","name":"Formal Methods for Stochastic Models: Algorithms and Applications"},{"call_identifier":"H2020","grant_number":"665385","_id":"2564DBCA-B435-11E9-9278-68D0E5697425","name":"International IST Doctoral Program"}],"quality_controlled":"1","year":"2023","status":"public","intvolume":" 13993","publication_identifier":{"isbn":["9783031308222"],"issn":["0302-9743"],"eissn":["1611-3349"]},"date_created":"2023-06-18T22:00:47Z","date_published":"2023-04-22T00:00:00Z","type":"conference","title":"A learner-verifier framework for neural network controllers and certificates of stochastic systems","corr_author":"1","oa_version":"Published Version","file_date_updated":"2023-06-19T08:29:30Z","has_accepted_license":"1","publication":"Tools and Algorithms for the Construction and Analysis of Systems ","month":"04","author":[{"full_name":"Chatterjee, Krishnendu","first_name":"Krishnendu","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","last_name":"Chatterjee","orcid":"0000-0002-4561-241X"},{"first_name":"Thomas A","full_name":"Henzinger, Thomas A","orcid":"0000-0002-2985-7724","last_name":"Henzinger","id":"40876CD8-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Lechner","id":"3DC22916-F248-11E8-B48F-1D18A9856A87","first_name":"Mathias","full_name":"Lechner, Mathias"},{"full_name":"Zikelic, Dorde","first_name":"Dorde","last_name":"Zikelic","id":"294AA7A6-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-4681-1699"}],"publisher":"Springer Nature","date_updated":"2024-10-09T21:05:48Z","volume":13993,"conference":{"name":"TACAS: Tools and Algorithms for the Construction and Analysis of Systems","location":"Paris, France","start_date":"2023-04-22","end_date":"2023-04-27"},"citation":{"chicago":"Chatterjee, Krishnendu, Thomas A Henzinger, Mathias Lechner, and Dorde Zikelic. “A Learner-Verifier Framework for Neural Network Controllers and Certificates of Stochastic Systems.” In Tools and Algorithms for the Construction and Analysis of Systems , 13993:3–25. Springer Nature, 2023. https://doi.org/10.1007/978-3-031-30823-9_1.","ista":"Chatterjee K, Henzinger TA, Lechner M, Zikelic D. 2023. A learner-verifier framework for neural network controllers and certificates of stochastic systems. Tools and Algorithms for the Construction and Analysis of Systems . TACAS: Tools and Algorithms for the Construction and Analysis of Systems, LNCS, vol. 13993, 3–25.","ama":"Chatterjee K, Henzinger TA, Lechner M, Zikelic D. A learner-verifier framework for neural network controllers and certificates of stochastic systems. In: Tools and Algorithms for the Construction and Analysis of Systems . Vol 13993. Springer Nature; 2023:3-25. doi:10.1007/978-3-031-30823-9_1","apa":"Chatterjee, K., Henzinger, T. A., Lechner, M., & Zikelic, D. (2023). A learner-verifier framework for neural network controllers and certificates of stochastic systems. In Tools and Algorithms for the Construction and Analysis of Systems (Vol. 13993, pp. 3–25). Paris, France: Springer Nature. https://doi.org/10.1007/978-3-031-30823-9_1","mla":"Chatterjee, Krishnendu, et al. “A Learner-Verifier Framework for Neural Network Controllers and Certificates of Stochastic Systems.” Tools and Algorithms for the Construction and Analysis of Systems , vol. 13993, Springer Nature, 2023, pp. 3–25, doi:10.1007/978-3-031-30823-9_1.","short":"K. Chatterjee, T.A. Henzinger, M. Lechner, D. Zikelic, in:, Tools and Algorithms for the Construction and Analysis of Systems , Springer Nature, 2023, pp. 3–25.","ieee":"K. Chatterjee, T. A. Henzinger, M. Lechner, and D. Zikelic, “A learner-verifier framework for neural network controllers and certificates of stochastic systems,” in Tools and Algorithms for the Construction and Analysis of Systems , Paris, France, 2023, vol. 13993, pp. 3–25."},"oa":1,"department":[{"_id":"KrCh"},{"_id":"ToHe"}],"article_processing_charge":"No","scopus_import":"1","file":[{"date_updated":"2023-06-19T08:29:30Z","creator":"dernst","file_size":528455,"success":1,"date_created":"2023-06-19T08:29:30Z","file_id":"13150","relation":"main_file","access_level":"open_access","checksum":"3d8a8bb24d211bc83360dfc2fd744307","content_type":"application/pdf","file_name":"2023_LNCS_Chatterjee.pdf"}],"day":"22","ec_funded":1,"abstract":[{"lang":"eng","text":"Reinforcement learning has received much attention for learning controllers of deterministic systems. We consider a learner-verifier framework for stochastic control systems and survey recent methods that formally guarantee a conjunction of reachability and safety properties. Given a property and a lower bound on the probability of the property being satisfied, our framework jointly learns a control policy and a formal certificate to ensure the satisfaction of the property with a desired probability threshold. Both the control policy and the formal certificate are continuous functions from states to reals, which are learned as parameterized neural networks. While in the deterministic case, the certificates are invariant and barrier functions for safety, or Lyapunov and ranking functions for liveness, in the stochastic case the certificates are supermartingales. For certificate verification, we use interval arithmetic abstract interpretation to bound the expected values of neural network functions."}],"alternative_title":["LNCS"],"language":[{"iso":"eng"}],"ddc":["000"],"page":"3-25","publication_status":"published","tmp":{"short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"_id":"13142","acknowledgement":"This work was supported in part by the ERC-2020-AdG 101020093, ERC CoG 863818 (FoRM-SMArt) and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 665385.","license":"https://creativecommons.org/licenses/by/4.0/","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"}