[{"title":"Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection","year":"2026","status":"public","author":[{"last_name":"Stenner","first_name":"Jan","full_name":"Stenner, Jan"},{"first_name":"Hans","last_name":"Harder","full_name":"Harder, Hans","id":"98879"},{"last_name":"Peitz","orcid":"0000-0002-3389-793X","first_name":"Sebastian","full_name":"Peitz, Sebastian","id":"47427"}],"date_updated":"2026-10-01T12:01:23Z","main_file_link":[{"url":"https://arxiv.org/abs/2606.30238","open_access":"1"}],"_id":"67306","language":[{"iso":"eng"}],"user_id":"47427","publication":"arXiv:2606.30238","citation":{"apa":"Stenner, J., Harder, H., &#38; Peitz, S. (2026). Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection. In <i>arXiv:2606.30238</i>.","ieee":"J. Stenner, H. Harder, and S. Peitz, “Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection,” <i>arXiv:2606.30238</i>. 2026.","chicago":"Stenner, Jan, Hans Harder, and Sebastian Peitz. “Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection.” <i>ArXiv:2606.30238</i>, 2026.","short":"J. Stenner, H. Harder, S. Peitz, ArXiv:2606.30238 (2026).","mla":"Stenner, Jan, et al. “Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection.” <i>ArXiv:2606.30238</i>, 2026.","ama":"Stenner J, Harder H, Peitz S. Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection. <i>arXiv:260630238</i>. Published online 2026.","bibtex":"@article{Stenner_Harder_Peitz_2026, title={Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection}, journal={arXiv:2606.30238}, author={Stenner, Jan and Harder, Hans and Peitz, Sebastian}, year={2026} }"},"date_created":"2026-10-01T12:00:58Z","keyword":["own","own-preprint","erc"],"type":"preprint","oa":"1","department":[{"_id":"655"}]},{"intvolume":"       331","date_updated":"2026-10-01T11:52:48Z","author":[{"id":"98879","full_name":"Harder, Hans","last_name":"Harder","first_name":"Hans"},{"full_name":"Vishwasrao, Abhijeet","last_name":"Vishwasrao","first_name":"Abhijeet"},{"first_name":"Luca","last_name":"Guastoni","full_name":"Guastoni, Luca"},{"full_name":"Vinuesa, Ricardo","first_name":"Ricardo","last_name":"Vinuesa"},{"id":"47427","full_name":"Peitz, Sebastian","orcid":"0000-0002-3389-793X","first_name":"Sebastian","last_name":"Peitz"}],"year":"2026","title":"Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems","doi":"10.48550/arXiv.2511.04641","series_title":"Proceedings of Machine Learning Research","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://openreview.net/pdf?id=Z9srtyqVLE","open_access":"1"}],"publication":"Proceedings of The 8th Annual Learning for Dynamics and Control Conference","department":[{"_id":"655"}],"type":"conference","keyword":["own","own-conference","erc"],"date_created":"2026-10-01T11:51:57Z","status":"public","volume":331,"editor":[{"first_name":"Gaurav","last_name":"Sukhatme","full_name":"Sukhatme, Gaurav"},{"first_name":"Lars","last_name":"Lindemann","full_name":"Lindemann, Lars"},{"full_name":"Tu, Stephen","first_name":"Stephen","last_name":"Tu"},{"full_name":"Wierman, Adam","first_name":"Adam","last_name":"Wierman"},{"full_name":"Atanasov, Nikolay","first_name":"Nikolay","last_name":"Atanasov"}],"user_id":"47427","publisher":"PMLR","_id":"67299","page":"1601–1619","citation":{"short":"H. Harder, A. Vishwasrao, L. Guastoni, R. Vinuesa, S. Peitz, in: G. Sukhatme, L. Lindemann, S. Tu, A. Wierman, N. Atanasov (Eds.), Proceedings of The 8th Annual Learning for Dynamics and Control Conference, PMLR, 2026, pp. 1601–1619.","chicago":"Harder, Hans, Abhijeet Vishwasrao, Luca Guastoni, Ricardo Vinuesa, and Sebastian Peitz. “Efficient Probabilistic Surrogate Modeling Techniques for Partially-Observed Large-Scale Dynamical Systems.” In <i>Proceedings of The 8th Annual Learning for Dynamics and Control Conference</i>, edited by Gaurav Sukhatme, Lars Lindemann, Stephen Tu, Adam Wierman, and Nikolay Atanasov, 331:1601–1619. Proceedings of Machine Learning Research. PMLR, 2026. <a href=\"https://doi.org/10.48550/arXiv.2511.04641\">https://doi.org/10.48550/arXiv.2511.04641</a>.","ieee":"H. Harder, A. Vishwasrao, L. Guastoni, R. Vinuesa, and S. Peitz, “Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems,” in <i>Proceedings of The 8th Annual Learning for Dynamics and Control Conference</i>, 2026, vol. 331, pp. 1601–1619, doi: <a href=\"https://doi.org/10.48550/arXiv.2511.04641\">10.48550/arXiv.2511.04641</a>.","apa":"Harder, H., Vishwasrao, A., Guastoni, L., Vinuesa, R., &#38; Peitz, S. (2026). Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems. In G. Sukhatme, L. Lindemann, S. Tu, A. Wierman, &#38; N. Atanasov (Eds.), <i>Proceedings of The 8th Annual Learning for Dynamics and Control Conference</i> (Vol. 331, pp. 1601–1619). PMLR. <a href=\"https://doi.org/10.48550/arXiv.2511.04641\">https://doi.org/10.48550/arXiv.2511.04641</a>","bibtex":"@inproceedings{Harder_Vishwasrao_Guastoni_Vinuesa_Peitz_2026, series={Proceedings of Machine Learning Research}, title={Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems}, volume={331}, DOI={<a href=\"https://doi.org/10.48550/arXiv.2511.04641\">10.48550/arXiv.2511.04641</a>}, booktitle={Proceedings of The 8th Annual Learning for Dynamics and Control Conference}, publisher={PMLR}, author={Harder, Hans and Vishwasrao, Abhijeet and Guastoni, Luca and Vinuesa, Ricardo and Peitz, Sebastian}, editor={Sukhatme, Gaurav and Lindemann, Lars and Tu, Stephen and Wierman, Adam and Atanasov, Nikolay}, year={2026}, pages={1601–1619}, collection={Proceedings of Machine Learning Research} }","ama":"Harder H, Vishwasrao A, Guastoni L, Vinuesa R, Peitz S. Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems. In: Sukhatme G, Lindemann L, Tu S, Wierman A, Atanasov N, eds. <i>Proceedings of The 8th Annual Learning for Dynamics and Control Conference</i>. Vol 331. Proceedings of Machine Learning Research. PMLR; 2026:1601–1619. doi:<a href=\"https://doi.org/10.48550/arXiv.2511.04641\">10.48550/arXiv.2511.04641</a>","mla":"Harder, Hans, et al. “Efficient Probabilistic Surrogate Modeling Techniques for Partially-Observed Large-Scale Dynamical Systems.” <i>Proceedings of The 8th Annual Learning for Dynamics and Control Conference</i>, edited by Gaurav Sukhatme et al., vol. 331, PMLR, 2026, pp. 1601–1619, doi:<a href=\"https://doi.org/10.48550/arXiv.2511.04641\">10.48550/arXiv.2511.04641</a>."},"oa":"1"},{"title":"Equivariance and partial observations in Koopman operator theory for partial differential equations","year":"2025","author":[{"id":"47427","full_name":"Peitz, Sebastian","orcid":"0000-0002-3389-793X","first_name":"Sebastian","last_name":"Peitz"},{"full_name":"Harder, Hans","last_name":"Harder","first_name":"Hans","id":"98879"},{"first_name":"Feliks","last_name":"Nüske","full_name":"Nüske, Feliks"},{"first_name":"Friedrich","last_name":"Philipp","full_name":"Philipp, Friedrich"},{"last_name":"Schaller","first_name":"Manuel","full_name":"Schaller, Manuel"},{"full_name":"Worthmann, Karl","last_name":"Worthmann","first_name":"Karl"}],"date_updated":"2026-10-01T11:32:34Z","intvolume":"        12","main_file_link":[{"open_access":"1","url":"https://arxiv.org/pdf/2307.15325"}],"language":[{"iso":"eng"}],"doi":"10.3934/jcd.2024035","publication":"Journal of Computational Dynamics","abstract":[{"lang":"eng","text":"The Koopman operator has become an essential tool for data-driven analysis, prediction and control of complex systems, the main reason being the enormous potential of identifying linear function space representations of nonlinear\r\ndynamics from measurements. Until now, the situation where for large-scale systems, we (i) only have access to partial observations (i.e., measurements, as is very common for experimental data) or (ii) deliberately perform coarse\r\ngraining (for efficiency reasons) has not been treated to its full extent. In this paper, we address the pitfall associated with this situation, that the classical EDMD algorithm does not automatically provide a Koopman operator approximation for the underlying system if we do not carefully select the number of observables. Moreover, we show that symmetries in the system dynamics can be carried over to the Koopman operator, which allows us to massively increase the model efficiency. We also briefly draw a connection to domain decomposition techniques for partial differential equations and present numerical evidence using the Kuramoto--Sivashinsky equation."}],"date_created":"2023-08-21T05:52:24Z","type":"journal_article","department":[{"_id":"655"}],"status":"public","page":"305-324","_id":"46579","user_id":"47427","volume":12,"citation":{"short":"S. Peitz, H. Harder, F. Nüske, F. Philipp, M. Schaller, K. Worthmann, Journal of Computational Dynamics 12 (2025) 305–324.","chicago":"Peitz, Sebastian, Hans Harder, Feliks Nüske, Friedrich Philipp, Manuel Schaller, and Karl Worthmann. “Equivariance and Partial Observations in Koopman Operator Theory for Partial Differential Equations.” <i>Journal of Computational Dynamics</i> 12 (2025): 305–24. <a href=\"https://doi.org/10.3934/jcd.2024035\">https://doi.org/10.3934/jcd.2024035</a>.","ieee":"S. Peitz, H. Harder, F. Nüske, F. Philipp, M. Schaller, and K. Worthmann, “Equivariance and partial observations in Koopman operator theory for partial differential equations,” <i>Journal of Computational Dynamics</i>, vol. 12, pp. 305–324, 2025, doi: <a href=\"https://doi.org/10.3934/jcd.2024035\">10.3934/jcd.2024035</a>.","apa":"Peitz, S., Harder, H., Nüske, F., Philipp, F., Schaller, M., &#38; Worthmann, K. (2025). Equivariance and partial observations in Koopman operator theory for partial differential equations. <i>Journal of Computational Dynamics</i>, <i>12</i>, 305–324. <a href=\"https://doi.org/10.3934/jcd.2024035\">https://doi.org/10.3934/jcd.2024035</a>","bibtex":"@article{Peitz_Harder_Nüske_Philipp_Schaller_Worthmann_2025, title={Equivariance and partial observations in Koopman operator theory for partial differential equations}, volume={12}, DOI={<a href=\"https://doi.org/10.3934/jcd.2024035\">10.3934/jcd.2024035</a>}, journal={Journal of Computational Dynamics}, author={Peitz, Sebastian and Harder, Hans and Nüske, Feliks and Philipp, Friedrich and Schaller, Manuel and Worthmann, Karl}, year={2025}, pages={305–324} }","ama":"Peitz S, Harder H, Nüske F, Philipp F, Schaller M, Worthmann K. Equivariance and partial observations in Koopman operator theory for partial differential equations. <i>Journal of Computational Dynamics</i>. 2025;12:305-324. doi:<a href=\"https://doi.org/10.3934/jcd.2024035\">10.3934/jcd.2024035</a>","mla":"Peitz, Sebastian, et al. “Equivariance and Partial Observations in Koopman Operator Theory for Partial Differential Equations.” <i>Journal of Computational Dynamics</i>, vol. 12, 2025, pp. 305–24, doi:<a href=\"https://doi.org/10.3934/jcd.2024035\">10.3934/jcd.2024035</a>."},"external_id":{"arxiv":["2307.15325"]},"oa":"1"},{"doi":"10.1007/978-3-031-83191-1_10","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2404.18530"}],"language":[{"iso":"eng"}],"date_updated":"2026-10-01T11:39:19Z","publication_status":"published","title":"Solving partial differential equations with equivariant extreme learning machines","year":"2025","author":[{"id":"98879","first_name":"Hans","last_name":"Harder","full_name":"Harder, Hans"},{"id":"47427","first_name":"Sebastian","orcid":"0000-0002-3389-793X","last_name":"Peitz","full_name":"Peitz, Sebastian"}],"publication_identifier":{"isbn":["978-3-031-83191-1"]},"keyword":["extreme learning machines","partial differential equations","data-driven prediction","high-dimensional systems"],"type":"conference","date_created":"2024-04-30T08:43:14Z","abstract":[{"text":"We utilize extreme learning machines for the prediction of partial differential equations (PDEs). Our method splits the state space into multiple windows that are predicted individually using a single model. Despite requiring only few data points (in some cases, our method can learn from a single full-state snapshot), it still achieves high accuracy and can predict the flow of PDEs over long time horizons. Moreover, we show how additional symmetries can be exploited to increase sample efficiency and to enforce equivariance.","lang":"eng"}],"publication":"Systems theory in data and optimization","user_id":"47427","editor":[{"first_name":"J.","last_name":"Berberich","full_name":"Berberich, J."},{"last_name":"Ianelli","first_name":"A.","full_name":"Ianelli, A."},{"first_name":"F.","last_name":"Allgöwer","full_name":"Allgöwer, F."}],"page":"153-168","_id":"53793","publisher":"Springer","status":"public","oa":"1","place":"Cham","citation":{"short":"H. Harder, S. Peitz, in: J. Berberich, A. Ianelli, F. Allgöwer (Eds.), Systems Theory in Data and Optimization, Springer, Cham, 2025, pp. 153–168.","chicago":"Harder, Hans, and Sebastian Peitz. “Solving Partial Differential Equations with Equivariant Extreme Learning Machines.” In <i>Systems Theory in Data and Optimization</i>, edited by J. Berberich, A. Ianelli, and F. Allgöwer, 153–68. Cham: Springer, 2025. <a href=\"https://doi.org/10.1007/978-3-031-83191-1_10\">https://doi.org/10.1007/978-3-031-83191-1_10</a>.","ieee":"H. Harder and S. Peitz, “Solving partial differential equations with equivariant extreme learning machines,” in <i>Systems theory in data and optimization</i>, 2025, pp. 153–168, doi: <a href=\"https://doi.org/10.1007/978-3-031-83191-1_10\">10.1007/978-3-031-83191-1_10</a>.","apa":"Harder, H., &#38; Peitz, S. (2025). Solving partial differential equations with equivariant extreme learning machines. In J. Berberich, A. Ianelli, &#38; F. Allgöwer (Eds.), <i>Systems theory in data and optimization</i> (pp. 153–168). Springer. <a href=\"https://doi.org/10.1007/978-3-031-83191-1_10\">https://doi.org/10.1007/978-3-031-83191-1_10</a>","bibtex":"@inproceedings{Harder_Peitz_2025, place={Cham}, title={Solving partial differential equations with equivariant extreme learning machines}, DOI={<a href=\"https://doi.org/10.1007/978-3-031-83191-1_10\">10.1007/978-3-031-83191-1_10</a>}, booktitle={Systems theory in data and optimization}, publisher={Springer}, author={Harder, Hans and Peitz, Sebastian}, editor={Berberich, J. and Ianelli, A. and Allgöwer, F.}, year={2025}, pages={153–168} }","ama":"Harder H, Peitz S. Solving partial differential equations with equivariant extreme learning machines. In: Berberich J, Ianelli A, Allgöwer F, eds. <i>Systems Theory in Data and Optimization</i>. Springer; 2025:153-168. doi:<a href=\"https://doi.org/10.1007/978-3-031-83191-1_10\">10.1007/978-3-031-83191-1_10</a>","mla":"Harder, Hans, and Sebastian Peitz. “Solving Partial Differential Equations with Equivariant Extreme Learning Machines.” <i>Systems Theory in Data and Optimization</i>, edited by J. Berberich et al., Springer, 2025, pp. 153–68, doi:<a href=\"https://doi.org/10.1007/978-3-031-83191-1_10\">10.1007/978-3-031-83191-1_10</a>."}},{"author":[{"full_name":"Harder, Hans","first_name":"Hans","last_name":"Harder","id":"98879"},{"orcid":"0000-0002-3389-793X","first_name":"Sebastian","last_name":"Peitz","full_name":"Peitz, Sebastian","id":"47427"},{"full_name":"Nüske, Feliks","first_name":"Feliks","last_name":"Nüske"},{"full_name":"Philipp, Friedrich","last_name":"Philipp","first_name":"Friedrich"},{"first_name":"Manuel","last_name":"Schaller","full_name":"Schaller, Manuel"},{"first_name":"Karl","last_name":"Worthmann","full_name":"Worthmann, Karl"}],"year":"2025","status":"public","title":"Group Convolutional Extended Dynamic Mode Decomposition","intvolume":"       480","date_updated":"2026-10-01T12:05:18Z","_id":"67314","language":[{"iso":"eng"}],"page":"134725","volume":480,"doi":"10.1016/j.physd.2025.134725","user_id":"47427","citation":{"mla":"Harder, Hans, et al. “Group Convolutional Extended Dynamic Mode Decomposition.” <i>Physica D: Nonlinear Phenomena</i>, vol. 480, 2025, p. 134725, doi:<a href=\"https://doi.org/10.1016/j.physd.2025.134725\">10.1016/j.physd.2025.134725</a>.","ama":"Harder H, Peitz S, Nüske F, Philipp F, Schaller M, Worthmann K. Group Convolutional Extended Dynamic Mode Decomposition. <i>Physica D: Nonlinear Phenomena</i>. 2025;480:134725. doi:<a href=\"https://doi.org/10.1016/j.physd.2025.134725\">10.1016/j.physd.2025.134725</a>","bibtex":"@article{Harder_Peitz_Nüske_Philipp_Schaller_Worthmann_2025, title={Group Convolutional Extended Dynamic Mode Decomposition}, volume={480}, DOI={<a href=\"https://doi.org/10.1016/j.physd.2025.134725\">10.1016/j.physd.2025.134725</a>}, journal={Physica D: Nonlinear Phenomena}, author={Harder, Hans and Peitz, Sebastian and Nüske, Feliks and Philipp, Friedrich and Schaller, Manuel and Worthmann, Karl}, year={2025}, pages={134725} }","apa":"Harder, H., Peitz, S., Nüske, F., Philipp, F., Schaller, M., &#38; Worthmann, K. (2025). Group Convolutional Extended Dynamic Mode Decomposition. <i>Physica D: Nonlinear Phenomena</i>, <i>480</i>, 134725. <a href=\"https://doi.org/10.1016/j.physd.2025.134725\">https://doi.org/10.1016/j.physd.2025.134725</a>","ieee":"H. Harder, S. Peitz, F. Nüske, F. Philipp, M. Schaller, and K. Worthmann, “Group Convolutional Extended Dynamic Mode Decomposition,” <i>Physica D: Nonlinear Phenomena</i>, vol. 480, p. 134725, 2025, doi: <a href=\"https://doi.org/10.1016/j.physd.2025.134725\">10.1016/j.physd.2025.134725</a>.","chicago":"Harder, Hans, Sebastian Peitz, Feliks Nüske, Friedrich Philipp, Manuel Schaller, and Karl Worthmann. “Group Convolutional Extended Dynamic Mode Decomposition.” <i>Physica D: Nonlinear Phenomena</i> 480 (2025): 134725. <a href=\"https://doi.org/10.1016/j.physd.2025.134725\">https://doi.org/10.1016/j.physd.2025.134725</a>.","short":"H. Harder, S. Peitz, F. Nüske, F. Philipp, M. Schaller, K. Worthmann, Physica D: Nonlinear Phenomena 480 (2025) 134725."},"publication":"Physica D: Nonlinear Phenomena","date_created":"2026-10-01T12:05:08Z","department":[{"_id":"655"}],"type":"journal_article","keyword":["own","own-journal"]},{"citation":{"ama":"Fromme F, Allen-Blanchette C, Harder H, Peitz S. Surrogate Modeling of 3D Rayleigh-Bénard Convection with Equivariant Autoencoders. <i>arXiv:250513569</i>. Published online 2025.","bibtex":"@article{Fromme_Allen-Blanchette_Harder_Peitz_2025, title={Surrogate Modeling of 3D Rayleigh-Bénard Convection with Equivariant Autoencoders}, journal={arXiv:2505.13569}, author={Fromme, Fynn and Allen-Blanchette, Christine and Harder, Hans and Peitz, Sebastian}, year={2025} }","mla":"Fromme, Fynn, et al. “Surrogate Modeling of 3D Rayleigh-Bénard Convection with Equivariant Autoencoders.” <i>ArXiv:2505.13569</i>, 2025.","short":"F. Fromme, C. Allen-Blanchette, H. Harder, S. Peitz, ArXiv:2505.13569 (2025).","chicago":"Fromme, Fynn, Christine Allen-Blanchette, Hans Harder, and Sebastian Peitz. “Surrogate Modeling of 3D Rayleigh-Bénard Convection with Equivariant Autoencoders.” <i>ArXiv:2505.13569</i>, 2025.","apa":"Fromme, F., Allen-Blanchette, C., Harder, H., &#38; Peitz, S. (2025). Surrogate Modeling of 3D Rayleigh-Bénard Convection with Equivariant Autoencoders. In <i>arXiv:2505.13569</i>.","ieee":"F. Fromme, C. Allen-Blanchette, H. Harder, and S. Peitz, “Surrogate Modeling of 3D Rayleigh-Bénard Convection with Equivariant Autoencoders,” <i>arXiv:2505.13569</i>. 2025."},"publication":"arXiv:2505.13569","department":[{"_id":"655"}],"keyword":["own","own-preprint","erc"],"type":"preprint","date_created":"2026-10-01T12:06:29Z","date_updated":"2026-10-01T12:06:45Z","author":[{"last_name":"Fromme","first_name":"Fynn","full_name":"Fromme, Fynn"},{"full_name":"Allen-Blanchette, Christine","last_name":"Allen-Blanchette","first_name":"Christine"},{"last_name":"Harder","first_name":"Hans","full_name":"Harder, Hans","id":"98879"},{"last_name":"Peitz","first_name":"Sebastian","orcid":"0000-0002-3389-793X","full_name":"Peitz, Sebastian","id":"47427"}],"title":"Surrogate Modeling of 3D Rayleigh-Bénard Convection with Equivariant Autoencoders","year":"2025","status":"public","user_id":"47427","_id":"67318","language":[{"iso":"eng"}]},{"date_updated":"2026-10-01T11:37:15Z","year":"2024","status":"public","title":"On the continuity and smoothness of the value function in reinforcement learning and optimal control","author":[{"last_name":"Harder","first_name":"Hans","full_name":"Harder, Hans","id":"98879"},{"full_name":"Peitz, Sebastian","first_name":"Sebastian","last_name":"Peitz","orcid":"0000-0002-3389-793X","id":"47427"}],"user_id":"47427","doi":"10.1109/CDC56724.2024.10886529","page":"1935-1940","language":[{"iso":"eng"}],"_id":"52758","publisher":"IEEE","publication":"IEEE 63rd conference on decision and control (CDC)","citation":{"ieee":"H. Harder and S. Peitz, “On the continuity and smoothness of the value function in reinforcement learning and optimal control,” in <i>IEEE 63rd conference on decision and control (CDC)</i>, 2024, pp. 1935–1940, doi: <a href=\"https://doi.org/10.1109/CDC56724.2024.10886529\">10.1109/CDC56724.2024.10886529</a>.","apa":"Harder, H., &#38; Peitz, S. (2024). On the continuity and smoothness of the value function in reinforcement learning and optimal control. <i>IEEE 63rd Conference on Decision and Control (CDC)</i>, 1935–1940. <a href=\"https://doi.org/10.1109/CDC56724.2024.10886529\">https://doi.org/10.1109/CDC56724.2024.10886529</a>","short":"H. Harder, S. Peitz, in: IEEE 63rd Conference on Decision and Control (CDC), IEEE, 2024, pp. 1935–1940.","chicago":"Harder, Hans, and Sebastian Peitz. “On the Continuity and Smoothness of the Value Function in Reinforcement Learning and Optimal Control.” In <i>IEEE 63rd Conference on Decision and Control (CDC)</i>, 1935–40. IEEE, 2024. <a href=\"https://doi.org/10.1109/CDC56724.2024.10886529\">https://doi.org/10.1109/CDC56724.2024.10886529</a>.","mla":"Harder, Hans, and Sebastian Peitz. “On the Continuity and Smoothness of the Value Function in Reinforcement Learning and Optimal Control.” <i>IEEE 63rd Conference on Decision and Control (CDC)</i>, IEEE, 2024, pp. 1935–40, doi:<a href=\"https://doi.org/10.1109/CDC56724.2024.10886529\">10.1109/CDC56724.2024.10886529</a>.","bibtex":"@inproceedings{Harder_Peitz_2024, title={On the continuity and smoothness of the value function in reinforcement learning and optimal control}, DOI={<a href=\"https://doi.org/10.1109/CDC56724.2024.10886529\">10.1109/CDC56724.2024.10886529</a>}, booktitle={IEEE 63rd conference on decision and control (CDC)}, publisher={IEEE}, author={Harder, Hans and Peitz, Sebastian}, year={2024}, pages={1935–1940} }","ama":"Harder H, Peitz S. On the continuity and smoothness of the value function in reinforcement learning and optimal control. In: <i>IEEE 63rd Conference on Decision and Control (CDC)</i>. IEEE; 2024:1935-1940. doi:<a href=\"https://doi.org/10.1109/CDC56724.2024.10886529\">10.1109/CDC56724.2024.10886529</a>"},"type":"conference","date_created":"2024-03-25T08:20:37Z"}]