{"intvolume":" 37","publisher":"Association for the Advancement of Artificial Intelligence","author":[{"first_name":"Dorde","id":"294AA7A6-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-4681-1699","full_name":"Zikelic, Dorde","last_name":"Zikelic"},{"first_name":"Mathias","id":"3DC22916-F248-11E8-B48F-1D18A9856A87","full_name":"Lechner, Mathias","last_name":"Lechner"},{"full_name":"Henzinger, Thomas A","orcid":"0000-0002-2985-7724","last_name":"Henzinger","first_name":"Thomas A","id":"40876CD8-F248-11E8-B48F-1D18A9856A87"},{"id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","first_name":"Krishnendu","last_name":"Chatterjee","full_name":"Chatterjee, Krishnendu","orcid":"0000-0002-4561-241X"}],"abstract":[{"text":"We study the problem of learning controllers for discrete-time non-linear stochastic dynamical systems with formal reach-avoid guarantees. This work presents the first method for providing formal reach-avoid guarantees, which combine and generalize stability and safety guarantees, with a tolerable probability threshold p in [0,1] over the infinite time horizon. Our method leverages advances in machine learning literature and it represents formal certificates as neural networks. In particular, we learn a certificate in the form of a reach-avoid supermartingale (RASM), a novel notion that we introduce in this work. Our RASMs provide reachability and avoidance guarantees by imposing constraints on what can be viewed as a stochastic extension of level sets of Lyapunov functions for deterministic systems. Our approach solves several important problems -- it can be used to learn a control policy from scratch, to verify a reach-avoid specification for a fixed control policy, or to fine-tune a pre-trained policy if it does not satisfy the reach-avoid specification. We validate our approach on 3 stochastic non-linear reinforcement learning tasks.","lang":"eng"}],"date_created":"2024-01-18T07:44:31Z","publication_identifier":{"eissn":["2374-3468"],"issn":["2159-5399"]},"title":"Learning control policies for stochastic systems with reach-avoid guarantees","conference":{"end_date":"2023-02-14","location":"Washington, DC, United States","start_date":"2023-02-07","name":"AAAI: Conference on Artificial Intelligence"},"keyword":["General Medicine"],"doi":"10.1609/aaai.v37i10.26407","article_processing_charge":"No","external_id":{"arxiv":["2210.05308"]},"department":[{"_id":"ToHe"},{"_id":"KrCh"}],"issue":"10","status":"public","project":[{"name":"Vigilant Algorithmic Monitoring of Software","grant_number":"101020093","call_identifier":"H2020","_id":"62781420-2b32-11ec-9570-8d9b63373d4d"},{"_id":"0599E47C-7A3F-11EA-A408-12923DDC885E","call_identifier":"H2020","name":"Formal Methods for Stochastic Models: Algorithms and Applications","grant_number":"863818"},{"_id":"2564DBCA-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","name":"International IST Doctoral Program","grant_number":"665385"}],"oa_version":"Preprint","related_material":{"record":[{"id":"14600","status":"public","relation":"earlier_version"}]},"type":"conference","publication":"Proceedings of the 37th AAAI Conference on Artificial Intelligence","volume":37,"citation":{"ama":"Zikelic D, Lechner M, Henzinger TA, Chatterjee K. Learning control policies for stochastic systems with reach-avoid guarantees. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence. Vol 37. Association for the Advancement of Artificial Intelligence; 2023:11926-11935. doi:10.1609/aaai.v37i10.26407","ista":"Zikelic D, Lechner M, Henzinger TA, Chatterjee K. 2023. Learning control policies for stochastic systems with reach-avoid guarantees. Proceedings of the 37th AAAI Conference on Artificial Intelligence. AAAI: Conference on Artificial Intelligence vol. 37, 11926–11935.","ieee":"D. Zikelic, M. Lechner, T. A. Henzinger, and K. Chatterjee, “Learning control policies for stochastic systems with reach-avoid guarantees,” in Proceedings of the 37th AAAI Conference on Artificial Intelligence, Washington, DC, United States, 2023, vol. 37, no. 10, pp. 11926–11935.","short":"D. Zikelic, M. Lechner, T.A. Henzinger, K. Chatterjee, in:, Proceedings of the 37th AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence, 2023, pp. 11926–11935.","chicago":"Zikelic, Dorde, Mathias Lechner, Thomas A Henzinger, and Krishnendu Chatterjee. “Learning Control Policies for Stochastic Systems with Reach-Avoid Guarantees.” In Proceedings of the 37th AAAI Conference on Artificial Intelligence, 37:11926–35. Association for the Advancement of Artificial Intelligence, 2023. https://doi.org/10.1609/aaai.v37i10.26407.","mla":"Zikelic, Dorde, et al. “Learning Control Policies for Stochastic Systems with Reach-Avoid Guarantees.” Proceedings of the 37th AAAI Conference on Artificial Intelligence, vol. 37, no. 10, Association for the Advancement of Artificial Intelligence, 2023, pp. 11926–35, doi:10.1609/aaai.v37i10.26407.","apa":"Zikelic, D., Lechner, M., Henzinger, T. A., & Chatterjee, K. (2023). Learning control policies for stochastic systems with reach-avoid guarantees. In Proceedings of the 37th AAAI Conference on Artificial Intelligence (Vol. 37, pp. 11926–11935). Washington, DC, United States: Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v37i10.26407"},"date_published":"2023-06-26T00:00:00Z","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.","year":"2023","month":"06","quality_controlled":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","day":"26","_id":"14830","page":"11926-11935","ec_funded":1,"language":[{"iso":"eng"}],"date_updated":"2024-01-22T14:08:29Z","publication_status":"published"}