{"type":"ScholarlyArticle","title":"Planning Multiple Epidemic Interventions with Reinforcement Learning","authors":[{"name":"Anh Mai","slug":"anh-mai","given":"Anh","family":"Mai","affiliation":"New York University Abu Dhabi"},{"name":"Nikunj Gupta","slug":"nikunj-gupta","given":"Nikunj","family":"Gupta","affiliation":"New York University"},{"name":"Azza Abouzied","slug":"azza-abouzied","given":"Azza","family":"Abouzied","affiliation":"New York University Abu Dhabi"},{"name":"Dennis Shasha","slug":"dennis-shasha","given":"Dennis","family":"Shasha","affiliation":"New York University"}],"abstract":"Combating an epidemic entails finding a plan that describes when and how to apply different interventions, such as mask-wearing mandates, vaccinations, school or workplace closures. An optimal plan will curb an epidemic with minimal loss of life, disease burden, and economic cost. Finding an optimal plan is an intractable computational problem in realistic settings. Policy-makers, however, would greatly benefit from tools that can efficiently search for plans that minimize disease and economic costs especially when considering multiple possible interventions over a continuous and complex action space given a continuous and equally complex state space. We formulate this problem as a Markov decision process. Our formulation is unique in its ability to represent multiple continuous interventions over any disease model defined by ordinary differential equations. We illustrate how to effectively apply state-of-the-art actor-critic reinforcement learning algorithms (PPO and SAC) to search for plans that minimize overall costs. We empirically evaluate the learning performance of these algorithms and compare their performance to hand-crafted baselines that mimic plans constructed by policy-makers. Our method outperforms baselines. Our work confirms the viability of a computational approach to support policy-makers.","doi":"10.24963/ijcai.2023/682","url":"https://ijcai.org/proceedings/2023/682","pdf":"https://ijcai.org/proceedings/2023/0682.pdf","markdown":"https://ijcai.org/proceedings/2023/682.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/682","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"AI for Good","track":"AI for Good","number":682,"pages":{"first":6147,"last":6155},"keywords":["AI for Good: Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Abdallah et al., 2022] Wejden Abdallah, Dalel Kanzari, Dorsaf Sallami, Kurosh Madani, and Khaled Ghedira. A deep reinforcement learning based decision-making approach for avoiding crowd situation within the case of covid'19 pandemic. Computational Intelligence, 38(2):416-437, 2022.","title":"A deep reinforcement learning based decision-making approach for avoiding crowd situation within the case of covid'19 pandemic","authors":"Abdallah","year":"2022","venue":"Computational Intelligence","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Andrychowicz et al., 2020] Marcin Andrychowicz, Anton Raichuk, Piotr Stanczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Léonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, and Olivier Bachem. What matters in on-policy reinforce- ment learning? A large-scale empirical study. CoRR, abs/2006.05990, 2020.","title":"What matters in on-policy reinforcement learning? A large-scale empirical study","authors":"Marcin Andrychowicz, Anton Raichuk, Piotr Stanczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Léonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Arango and Pelov, 2020] Mauricio Arango and Lyudmil Pelov. Covid-19 pandemic cyclic lockdown optimiza- tion using reinforcement learning. arXiv preprint arXiv:2009.04647, 2020.","title":"Covid-19 pandemic cyclic lockdown optimization using reinforcement learning","authors":"Mauricio Arango, Lyudmil Pelov","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bampa et al., 2022] Maria Bampa, Tobias Fasth, Sindri Magnusson, and Panagiotis Papapetrou. : Learning inter- vention strategies for epidemics with reinforcement learn- ing. In International Conference on Artificial Intelligence in Medicine, pages 189-199. Springer, 2022.","title":"Learning intervention strategies for epidemics with reinforcement learning","authors":"Maria Bampa, Tobias Fasth, Sindri Magnusson, Panagiotis Papapetrou","year":"2022","venue":"International Conference on Artificial Intelligence in Medicine","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Barlow and Weinstein, 2020] N. S. Barlow and S. J. Wein- stein. Accurate closed-form solution of the SIR epidemic model. Physica D, 408:132540, Jul 2020.","title":"Accurate closed-form solution of the SIR epidemic model","authors":"N S Barlow, S J Weinstein","year":"2020","venue":"Physica D","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Bastani et al., 2021] Hamsa Bastani, Kimon Drakopoulos, Vishal Gupta, Ioannis Vlachogiannis, Christos Had- jichristodoulou, Pagona Lagiou, Gkikas Magiorkinis, Dimitrios Paraskevis, and Sotirios Tsiodras. Efficient and targeted covid-19 border testing via reinforcement learn- ing. Nature, 599(7883):108-113, 2021.","title":"Efficient and targeted covid-19 border testing via reinforcement learning","authors":"Hamsa Bastani, Kimon Drakopoulos, Vishal Gupta, Ioannis Vlachogiannis, Christos Hadjichristodoulou, Pagona Lagiou, Gkikas Magiorkinis, Dimitrios Paraskevis, Sotirios Tsiodras","year":"2021","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Bushaj et al., 2022] Sabah Bushaj, Xuecheng Yin, Arjeta Beqiri, Donald Andrews, and İ Esra Büyüktahtakın. A simulation-deep reinforcement learning (sirl) approach for epidemic control optimization. Annals of Operations Re- search, pages 1-33, 2022.","title":"A simulation-deep reinforcement learning (sirl) approach for epidemic control optimization","authors":"Bushaj","year":"2022","venue":"Annals of Operations Research","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Chadi and Mousannif, 2022] Mohamed-Amine Chadi and Hajar Mousannif. A reinforcement learning based deci- sion support tool for epidemic control: Validation study for covid-19. Applied Artificial Intelligence, pages 1-33, 2022.","title":"A reinforcement learning based decision support tool for epidemic control: Validation study for covid-19","authors":"Mohamed-Amine Chadi, Hajar Mousannif","year":"2022","venue":"Applied Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Colas et al., 2020] Cédric Colas, Boris P. Hejblum, Sébastien Rouillon, Rodolphe Thiébaut, Pierre-Yves Oudeyer, Clément Moulin-Frier, and Mélanie Prague. Epidemioptim: A toolbox for the optimization of control policies in epidemiological models. CoRR, abs/2010.04452, 2020.","title":"Epidemioptim: A toolbox for the optimization of control policies in epidemiological models","authors":"Cédric Colas, Boris P Hejblum, Sébastien Rouillon, Rodolphe Thiébaut, Pierre-Yves Oudeyer, Clément Moulin-Frier, Mélanie Prague","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Du et al., 2022] Xinqi Du, Tianyi Liu, Songwei Zhao, Jiu- man Song, and Hechang Chen. District-coupled epidemic control via deep reinforcement learning. In International Conference on Knowledge Science, Engineering and Man- agement, pages 417-428. Springer, 2022.","title":"District-coupled epidemic control via deep reinforcement learning","authors":"Du","year":"2022","venue":"International Conference on Knowledge Science, Engineering and Management","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Feng et al., 2022a] Tao Feng, Sirui Song, Tong Xia, and Yong Li. Contact tracing and epidemic intervention via deep reinforcement learning. ACM Transactions on Knowledge Discovery from Data (TKDD), 2022.","title":"Contact tracing and epidemic intervention via deep reinforcement learning","authors":"Feng","year":"2022","venue":"ACM Transactions on Knowledge Discovery from Data","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Feng et al., 2022b] Tao Feng, Tong Xia, Xiaochen Fan, Huandong Wang, Zefang Zong, and Yong Li. Precise mo- bility intervention for epidemic control using unobservable information via deep reinforcement learning. In Proceed- ings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 2882-2892, 2022.","title":"Precise mobility intervention for epidemic control using unobservable information via deep reinforcement learning","authors":"Feng","year":"2022","venue":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Ferguson et al., 2020] Neil Ferguson, Daniel Laydon, Gemma Nedjati-Gilani, and et al. Impact of non- pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand, pages 1-20. Imperial College London, 2020.","title":"Impact of nonpharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand","authors":"Neil Ferguson, Daniel Laydon, Gemma Nedjati-Gilani","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Haarnoja et al., 2018] Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. CoRR, abs/1801.01290, 2018.","title":"Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor","authors":"Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Hale et al., 2021] Thomas Hale, Noam Angrist, Rafael Goldszmidt, Beatriz Kira, Anna Petherick, Toby Phillips, Samuel Webster, Emily Cameron-Blake, Laura Hallas, Saptarshi Majumdar, and Helen Tatlow. A global panel database of pandemic policies (oxford covid-19 govern- ment response tracker). Nature Human Behaviour, 5, 04 2021.","title":"A global panel database of pandemic policies (oxford covid-19 government response tracker)","authors":"Thomas Hale, Noam Angrist, Rafael Goldszmidt, Beatriz Kira, Anna Petherick, Toby Phillips, Samuel Webster, Emily Cameron-Blake, Laura Hallas, Saptarshi Majumdar, Helen Tatlow","year":"2021","venue":"Nature Human Behaviour","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Hazelbag et al., 2020] C Marijn Hazelbag, Jonathan Dushoff, Emanuel M Dominic, Zinhle E Mthombothi, and Wim Delva. Calibration of individual-based models to epidemiological data: A systematic review. PLoS Comput. Biol., 16(5):e1007893, May 2020.","title":"Calibration of individual-based models to epidemiological data: A systematic review","authors":"Jonathan C Marijn Hazelbag, Dushoff, Zinhle E Emanuel M Dominic, Wim Mthombothi, Delva","year":"2020","venue":"PLoS Comput. Biol","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Huang et al., 2022] Shengyi Huang, Rousslan Fer- nand Julien Dossa, Antonin Raffin, Anssi Kanervisto, and Weixun Wang. The 37 implementation details of proximal policy optimization. In ICLR Blog Track, 2022. https://iclr-blog-track.github.io/2022/03/25/ppo- implementation-details/.","title":"The 37 implementation details of proximal policy optimization","authors":"Shengyi Huang, Rousslan Fernand, Julien Dossa, Antonin Raffin, Anssi Kanervisto, Weixun Wang","year":"2022","venue":"ICLR Blog Track","doi":null,"url":"https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/","internal_url":null},{"position":18,"raw":"[Jiang et al., 2020] Yuanshuang Jiang, Linfang Hou, Yuxi- ang Liu, Zhuoye Ding, Yong Zhang, and Shengzhong Feng. Epidemic control based on reinforcement learning approaches. 2020.","title":"Epidemic control based on reinforcement learning approaches","authors":"Yuanshuang Jiang, Linfang Hou, Yuxiang Liu, Zhuoye Ding, Yong Zhang, Shengzhong Feng","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Karim et al., 2021] S.S.A Karim et al. Omicron SARS- CoV-2 variant: a new chapter in the COVID-19 pandemic. Lancet, 398(10317):2126-2128, 12 2021.","title":"Omicron SARS-CoV-2 variant: a new chapter in the COVID-19 pandemic","authors":"S S Karim","year":"2021","venue":"Lancet","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Khadilkar et al., 2020] Harshad Khadilkar, Tanuja Ganu, and Deva P Seetharam. Optimising lockdown policies for epidemic control using reinforcement learning. Trans- actions of the Indian National Academy of Engineering, 5(2):129-132, 2020.","title":"Optimising lockdown policies for epidemic control using reinforcement learning","authors":"Harshad Khadilkar, Tanuja Ganu, Deva P Seetharam","year":"2020","venue":"Transactions of the Indian National Academy of Engineering","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Kleczkowski et al., 2012] Adam Kleczkowski, Katarzyna Oleś, Ewa Gudowska-Nowak, and Christopher A Gilligan. Searching for the most cost-effective strategy for control- ling epidemics spreading on regular and small-world net- works. J. R. Soc. Interface, 9(66):158-169, January 2012.","title":"Searching for the most cost-effective strategy for controlling epidemics spreading on regular and small-world networks","authors":"Adam Kleczkowski, Katarzyna Oleś, Ewa Gudowska-Nowak, Christopher A Gilligan","year":"2012","venue":"J. R. Soc. Interface","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Kocsis and Szepesvári, 2006] Levente Kocsis and Csaba Szepesvári. Bandit based monte-carlo planning. In Pro- ceedings of the 17th European Conference on Machine Learning, ECML'06, page 282-293, Berlin, Heidelberg, 2006. Springer-Verlag.","title":"Bandit based monte-carlo planning","authors":"Szepesvári Kocsis, Levente Kocsis, Csaba Szepesvári","year":"2006","venue":"Proceedings of the 17th European Conference on Machine Learning, ECML'06","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Kompella et al., 2020] Varun Kompella, Roberto Capo- bianco, Stacy Jong, Jonathan Browne, Spencer J. Fox, Lauren Ancel Meyers, Peter R. Wurman, and Peter Stone. Reinforcement learning for optimization of COVID-19 mitigation policies. CoRR, abs/2010.10560, 2020.","title":"Reinforcement learning for optimization of COVID-19 mitigation policies","authors":"Varun Kompella, Roberto Capobianco, Stacy Jong, Jonathan Browne, Spencer J Fox, Lauren Ancel Meyers, Peter R Wurman, Peter Stone","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Konda and Tsitsiklis, 1999] Vijay Konda and John Tsitsik- lis. Actor-critic algorithms. In S. Solla, T. Leen, and K. Müller, editors, Advances in Neural Information Pro- cessing Systems, volume 12. MIT Press, 1999.","title":"Actor-critic algorithms","authors":"Vijay Konda, John Tsitsiklis","year":"1999","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Kwak et al., 2021] Gloria Hyunjung Kwak, Lowell Ling, and Pan Hui. Deep reinforcement learning approaches for global public health strategies for covid-19 pandemic. PloS one, 16(5):e0251550, 2021.","title":"Deep reinforcement learning approaches for global public health strategies for covid-19 pandemic","authors":"Kwak","year":"2021","venue":"PloS one","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Lee et al., 2019] Ye-Rin Lee, Bogeum Cho, Min-Woo Jo, Minsu Ock, Donghoon Lee, Doungkyu Lee, Moon Jung Kim, and In-Hwan Oh. Measuring the economic bur- den of disease and injury in korea, 2015. Journal of Ko- rean medical science, 34(Suppl 1):e80-e80, Mar 2019. 30923489[pmid].","title":"Measuring the economic burden of disease and injury in korea","authors":"Lee","year":"2015","venue":"Journal of Korean medical science","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Lefèvre, 1981] Claude Lefèvre. Optimal control of a birth and death epidemic process. Operations Research, 29(5):971-982, 1981.","title":"Optimal control of a birth and death epidemic process","authors":"Claude Lefèvre, Lefèvre","year":"1981","venue":"Operations Research","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Libin et al., 2020] Pieter Libin, Arno Moonens, Timothy Verstraeten, Fabian Perez-Sanjines, Niel Hens, Philippe Lemey, and Ann Nowé. Deep reinforcement learning for large-scale epidemic control. CoRR, abs/2003.13676, 2020.","title":"Deep reinforcement learning for large-scale epidemic control","authors":"Pieter Libin, Arno Moonens, Timothy Verstraeten, Fabian Perez-Sanjines, Niel Hens, Philippe Lemey, Ann Nowé","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Littman et al., 2013] Michael L. Littman, Thomas L. Dean, and Leslie Pack Kaelbling. On the complexity of solving markov decision problems. CoRR, abs/1302.4971, 2013.","title":"On the complexity of solving markov decision problems","authors":"L Michael, Thomas L Littman, Leslie Dean, Kaelbling Pack","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Liu, 2020] Changliu Liu. A microscopic epidemic model and pandemic prediction using multi-agent reinforcement learning. arXiv preprint arXiv:2004.12959, 2020.","title":"Changliu Liu. A microscopic epidemic model and pandemic prediction using multi-agent reinforcement learning","authors":"Liu","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Maharaj and Kleczkowski, 2012] Savi Maharaj and Adam Kleczkowski. Controlling epidemic spread by social dis- tancing: do it well or not at all. BMC Public Health, 12(1):679, August 2012.","title":"Controlling epidemic spread by social distancing: do it well or not at all","authors":"Savi Maharaj, Adam Kleczkowski","year":"2012","venue":"BMC Public Health","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Mai et al., 2022] Anh Le Xuan Mai, Miro Mannino, Zain Tariq, Azza Abouzied, and Dennis Shasha. Epipolicy: A tool for combating epidemics. XRDS, 28(2):24-29, jan 2022.","title":"Epipolicy: A tool for combating epidemics","authors":"Anh Le, Xuan Mai, Miro Mannino, Zain Tariq, Azza Abouzied, Dennis Shasha","year":"2022","venue":"XRDS","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Mai et al., 2023] Anh Mai, Nikunj Gupta, Azza Abouzied, and Dennis E. Shasha. Planning multiple epidemic interventions with reinforcement learning. CoRR, abs/2301.12802, 2023.","title":"Planning multiple epidemic interventions with reinforcement learning","authors":"Anh Mai, Nikunj Gupta, Azza Abouzied, Dennis E Shasha","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Obsu and Balcha, 2020] Legesse Lemecha Obsu and Shiferaw Feyissa Balcha. Optimal control strategies for the transmission risk of covid-19. Journal of Biological Dynamics, 14(1):590-607, 2020. PMID: 32696723.","title":"Legesse Lemecha Obsu and Shiferaw Feyissa Balcha. Optimal control strategies for the transmission risk of covid-19","authors":"Balcha Obsu","year":"2020","venue":"Journal of Biological Dynamics","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Ohi et al., 2020] Abu Quwsar Ohi, MF Mridha, Muham- mad Mostafa Monowar, Md Hamid, et al. Exploring opti- mal control of epidemic spread using reinforcement learn- ing. Scientific reports, 10(1):1-19, 2020.","title":"Exploring optimal control of epidemic spread using reinforcement learning","authors":"Ohi","year":"2020","venue":"Scientific reports","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Oleś et al., 2012] Katarzyna Oleś, Ewa Gudowska-Nowak, and Adam Kleczkowski. Understanding disease control: influence of epidemiological and economic factors. PLoS One, 7(5):e36026, May 2012.","title":"Understanding disease control: influence of epidemiological and economic factors","authors":"Katarzyna Oleś, Ewa Gudowska-Nowak, Adam Kleczkowski","year":"2012","venue":"PLoS One","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Padmanabhan et al., 2021] Regina Padmanabhan, Nader Meskin, Tamer Khattab, Mujahed Shraim, and Mo- hammed Al-Hitmi. Reinforcement learning-based deci- sion support system for covid-19. Biomedical Signal Pro- cessing and Control, 68:102676, 2021.","title":"Reinforcement learning-based decision support system for covid-19","authors":"Regina Padmanabhan, Nader Meskin, Tamer Khattab, Mujahed Shraim, Mohammed Al-Hitmi","year":"2021","venue":"Biomedical Signal Processing and Control","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Perkins and España, 2020] T. A. Perkins and G. España. Optimal Control of the COVID-19 Pandemic with Non- pharmaceutical Interventions. Bull Math Biol, 82(9):118, 09 2020.","title":"Optimal Control of the COVID-19 Pandemic with Nonpharmaceutical Interventions","authors":"T A Perkins, G España","year":null,"venue":"Bull Math Biol","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Price et al., 2022] Ashley M Price, Brendan Flannery, H Keipp Talbot, and Grijalva. Influenza Vaccine Effec- tiveness Against Influenza A(H3N2)-Related Illness in the United States During the 2021-2022 Influenza Season. Clinical Infectious Diseases, 76(8):1358-1363, 12 2022.","title":"Influenza Vaccine Effectiveness Against Influenza A(H3N2)-Related Illness in the United States During the 2021-2022 Influenza Season","authors":"Brendan Ashley M Price, H Keipp Flannery, Grijalva Talbot","year":"2022","venue":"Clinical Infectious Diseases","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Raffin et al., 2021] Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, and Noah Dormann. Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research, 22(268):1-8, 2021.","title":"Stable-baselines3: Reliable reinforcement learning implementations","authors":"Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, Noah Dormann","year":"2021","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Rao and Jelvis, 2022] Ashwin Rao and Tikhon Jelvis. Foun- dations of Reinforcement Learning with Applications in Finance. Chapman and Hall/CRC, October 2022.","title":"Foundations of Reinforcement Learning with Applications in Finance","authors":"Ashwin Rao, Tikhon Jelvis","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Ritto et al., 2021] Thiago G Ritto, Americo Cunha Jr, and David A.W. Barton. Parameter calibration and uncertainty quantification in an SEIR-type COVID-19 model using ap- proximate Bayesian computation. In 3rd Pan American Congress on Computational Mechanics (PANACM 2021), Rio de Janeiro, Brazil, November 2021.","title":"Parameter calibration and uncertainty quantification in an SEIR-type COVID-19 model using approximate Bayesian computation","authors":"Americo Thiago G Ritto, David A W Cunha, Barton","year":"2021","venue":"3rd Pan American Congress on Computational Mechanics (PANACM 2021)","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Schulman et al., 2017] John Schulman, Filip Wolski, Pra- fulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. CoRR, abs/1707.06347, 2017.","title":"Proximal policy optimization algorithms","authors":"John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Song et al., 2020] Sirui Song, Zefang Zong, Yong Li, Xue Liu, and Yang Yu. Reinforced epidemic control: Saving both lives and economy. arXiv preprint arXiv:2008.01257, 2020.","title":"Reinforced epidemic control: Saving both lives and economy","authors":"Sirui Song, Zefang Zong, Yong Li, Xue Liu, Yang Yu","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Tariq et al., 2021] Zain Tariq, Miro Mannino, Mai Le Xuan Anh, Whitney Bagge, Azza Abouzied, and Dennis Shasha. Planning Epidemic Interventions with EpiPolicy, page 894-909. Association for Computing Machinery, New York, NY, USA, 2021.","title":"Planning Epidemic Interventions with EpiPolicy","authors":"Zain Tariq, Miro Mannino, Mai Le, Xuan Anh, Whitney Bagge, Azza Abouzied, Dennis Shasha","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Wang et al., 2019] Tingwu Wang, Xuchan Bao, Ignasi Clavera, Jerrick Hoang, Yeming Wen, Eric Langlois, Shunshi Zhang, Guodong Zhang, Pieter Abbeel, and Jimmy Ba. Benchmarking model-based reinforcement learning. CoRR, abs/1907.02057, 2019.","title":"Benchmarking model-based reinforcement learning","authors":"Wang","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Zong and Luo, 2022] Kai Zong and Cuicui Luo. Reinforce- ment learning based framework for covid-19 resource allo- cation. Computers & Industrial Engineering, 167:107960, 2022.","title":"Reinforcement learning based framework for covid-19 resource allocation","authors":"Luo ; Zong, Kai Zong, Cuicui Luo","year":"2022","venue":"Computers & Industrial Engineering","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}