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Learning By Doing: Controlling a Dynamical System using Control Theory, Reinforcement Learning, or Causality + Q&A
Sebastian Weichwald · Niklas Pfister · Dominik Baumann · Isabelle Guyon · Oliver Kroemer · Tabitha Lee · Søren Wengel Mogensen · Jonas Peters · Sebastian Trimpe

Wed Dec 08 02:05 AM -- 02:25 AM (PST) @ None
Event URL: https://learningbydoingcompetition.github.io »

Control theory, reinforcement learning, and causality are all ways of mathematically describing how the world changes when we interact with it. Each field offers a different perspective with its own strengths and weaknesses. In this competition, we aim to bring together researchers from all three fields to encourage cross-disciplinary discussions. The competition is constructed to readily fit into the mathematical frameworks of all three fields and participants of any background are encouraged to participate. We designed two tracks that consider a dynamical system for which participants need to find controls/policies to optimally interact with a target process: an open loop/bandit track and a closed loop/online RL track.

Author Information

Sebastian Weichwald (University of Copenhagen)
Niklas Pfister (University of Copenhagen)
Dominik Baumann (RWTH Aachen University)
Isabelle Guyon (UPSud, INRIA, University Paris-saclay and ChaLearn)
Oliver Kroemer (Carnegie Mellon University)
Tabitha Lee (Carnegie Mellon University)
Søren Wengel Mogensen (Lund University)
Jonas Peters (University of Copenhagen)
Sebastian Trimpe (RWTH Aachen University)

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