Foundations of RL and Control
Connections and New Perspectives

Workshop at the International Conference on Machine Learning (ICML) 2024 in Vienna, Austria

Despite rapid advances in machine learning, solving large-scale stochastic dynamic programming problems remains a significant challenge. The combination of neural networks with RL has opened new avenues for algorithm design, but the lack of theoretical guarantees of these approaches hinders their applicability to high-stake problems traditionally addressed using control theory, such as online supply chain optimization, industrial automation, and adaptive transportation systems. This workshop focuses on recent advances in developing a learning theory of decision (control) systems, that builds on techniques and concepts from two communities that have had limited interactions despite their shared target: reinforcement learning and control theory.

News

May 25, 2024 The submission deadline has been extended to May 29, 2024 (AoE).


Confirmed Speakers

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Martha White

University of Alberta

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Florian Dörfler

ETH Zürich

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Max Simchowitz

MIT

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Elad Hazan

Princeton University

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Maryam Kamgarpour

EPFL

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Sarah Dean

Cornell University

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Miroslav Krstic

UC San Diego



Organizers

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Claire Vernade

University of Tübingen

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Michael Muehlebach

MPI for Intelligent Systems

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Johannes Kirschner

Swiss Data Science Center

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Andreas Krause

ETH Zürich

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Csaba Szepesvári

University of Alberta,
Google DeepMind

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Dylan Foster

Microsoft Research

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Alexandre Proutière

KTH

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Onno Eberhard

MPI for Intelligent Systems,
University of Tübingen



Contact

If you have any questions, please contact us at forlac.workshop@gmail.com.