{"language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","quality_controlled":"1","day":"03","month":"04","page":"9794-9801","_id":"15055","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2002.12086","open_access":"1"}],"date_updated":"2024-03-04T08:30:16Z","publication_status":"published","article_type":"original","doi":"10.1609/aaai.v34i06.6531","conference":{"name":"AAAI: Conference on Artificial Intelligence","start_date":"2020-02-07","location":"New York, NY, United States","end_date":"2020-02-12"},"title":"Reinforcement learning of risk-constrained policies in Markov decision processes","keyword":["General Medicine"],"department":[{"_id":"KrCh"}],"external_id":{"arxiv":["2002.12086"]},"article_processing_charge":"No","author":[{"first_name":"Tomáš","full_name":"Brázdil, Tomáš","last_name":"Brázdil"},{"orcid":"0000-0002-4561-241X","full_name":"Chatterjee, Krishnendu","last_name":"Chatterjee","first_name":"Krishnendu","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Petr","full_name":"Novotný, Petr","last_name":"Novotný"},{"full_name":"Vahala, Jiří","last_name":"Vahala","first_name":"Jiří"}],"intvolume":"        34","publisher":"Association for the Advancement of Artificial Intelligence","publication_identifier":{"issn":["2374-3468"]},"oa":1,"abstract":[{"text":"<jats:p>Markov decision processes (MDPs) are the defacto framework for sequential decision making in the presence of stochastic uncertainty. A classical optimization criterion for MDPs is to maximize the expected discounted-sum payoff, which ignores low probability catastrophic events with highly negative impact on the system. On the other hand, risk-averse policies require the probability of undesirable events to be below a given threshold, but they do not account for optimization of the expected payoff. We consider MDPs with discounted-sum payoff with failure states which represent catastrophic outcomes. The objective of risk-constrained planning is to maximize the expected discounted-sum payoff among risk-averse policies that ensure the probability to encounter a failure state is below a desired threshold. Our main contribution is an efficient risk-constrained planning algorithm that combines UCT-like search with a predictor learned through interaction with the MDP (in the style of AlphaZero) and with a risk-constrained action selection via linear programming. We demonstrate the effectiveness of our approach with experiments on classical MDPs from the literature, including benchmarks with an order of 106 states.</jats:p>","lang":"eng"}],"date_created":"2024-03-04T08:07:22Z","volume":34,"citation":{"ieee":"T. Brázdil, K. Chatterjee, P. Novotný, and J. Vahala, “Reinforcement learning of risk-constrained policies in Markov decision processes,” <i>Proceedings of the 34th AAAI Conference on Artificial Intelligence</i>, vol. 34, no. 06. Association for the Advancement of Artificial Intelligence, pp. 9794–9801, 2020.","mla":"Brázdil, Tomáš, et al. “Reinforcement Learning of Risk-Constrained Policies in Markov Decision Processes.” <i>Proceedings of the 34th AAAI Conference on Artificial Intelligence</i>, vol. 34, no. 06, Association for the Advancement of Artificial Intelligence, 2020, pp. 9794–801, doi:<a href=\"https://doi.org/10.1609/aaai.v34i06.6531\">10.1609/aaai.v34i06.6531</a>.","short":"T. Brázdil, K. Chatterjee, P. Novotný, J. Vahala, Proceedings of the 34th AAAI Conference on Artificial Intelligence 34 (2020) 9794–9801.","chicago":"Brázdil, Tomáš, Krishnendu Chatterjee, Petr Novotný, and Jiří Vahala. “Reinforcement Learning of Risk-Constrained Policies in Markov Decision Processes.” <i>Proceedings of the 34th AAAI Conference on Artificial Intelligence</i>. Association for the Advancement of Artificial Intelligence, 2020. <a href=\"https://doi.org/10.1609/aaai.v34i06.6531\">https://doi.org/10.1609/aaai.v34i06.6531</a>.","ista":"Brázdil T, Chatterjee K, Novotný P, Vahala J. 2020. Reinforcement learning of risk-constrained policies in Markov decision processes. Proceedings of the 34th AAAI Conference on Artificial Intelligence. 34(06), 9794–9801.","ama":"Brázdil T, Chatterjee K, Novotný P, Vahala J. Reinforcement learning of risk-constrained policies in Markov decision processes. <i>Proceedings of the 34th AAAI Conference on Artificial Intelligence</i>. 2020;34(06):9794-9801. doi:<a href=\"https://doi.org/10.1609/aaai.v34i06.6531\">10.1609/aaai.v34i06.6531</a>","apa":"Brázdil, T., Chatterjee, K., Novotný, P., &#38; Vahala, J. (2020). Reinforcement learning of risk-constrained policies in Markov decision processes. <i>Proceedings of the 34th AAAI Conference on Artificial Intelligence</i>. New York, NY, United States: Association for the Advancement of Artificial Intelligence. <a href=\"https://doi.org/10.1609/aaai.v34i06.6531\">https://doi.org/10.1609/aaai.v34i06.6531</a>"},"date_published":"2020-04-03T00:00:00Z","publication":"Proceedings of the 34th AAAI Conference on Artificial Intelligence","type":"journal_article","year":"2020","acknowledgement":"Krishnendu Chatterjee is supported by the Austrian Science Fund (FWF) NFN Grant No. S11407-N23 (RiSE/SHiNE), and COST Action GAMENET. Tomas Brazdil is supported by the Grant Agency of Masaryk University grant no. MUNI/G/0739/2017 and by the Czech Science Foundation grant No. 18-11193S. Petr Novotny and Jirı Vahala are supported by the Czech Science Foundation grant No. GJ19-15134Y.","status":"public","issue":"06","project":[{"_id":"25863FF4-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","name":"Game Theory","grant_number":"S11407"}],"oa_version":"Preprint"}