Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning

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Questions in causality, control, and reinforcement learning go beyond the classical machine learning task of prediction under i.i.d. observations. Instead, these fields consider the problem of learning how to actively perturb a system to achieve a certain effect on a response variable. Arguably, they have complementary views on the problem: In control, one usually aims to first identify the system by excitation strategies to then apply model-based design techniques to control the system. In (non-model-based) reinforcement learning, one directly optimizes a reward. In causality, one focus is on identifiability of causal structure. We believe that combining the different views might create synergies and this competition is meant as a first step toward such synergies. The participants had access to observational and (offline) interventional data generated by dynamical systems. Track CHEM considers an open-loop problem in which a single impulse at the beginning of the dynamics can be set, while Track ROBO considers a closed-loop problem in which control variables can be set at each time step. The goal in both tracks is to infer controls that drive the system to a desired state. Code is open-sourced ( https://github.com/LearningByDoingCompetition/learningbydoing-comp ) to reproduce the winning solutions of the competition and to facilitate trying out new methods on the competition tasks.
Cite this Paper
Original languageEnglish
Title of host publicationProceedings of the NeurIPS 2021 Competitions and Demonstrations Track
Publication date2022
Publication statusPublished - 2022
Event35th Conference on Neural Information Processing Systems (NeurIPS 2021) - Virtuel
Duration: 6 Dec 202114 Dec 2021


Conference35th Conference on Neural Information Processing Systems (NeurIPS 2021)
SeriesProceedings of Machine Learning Research

ID: 345421821