Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning
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, the...
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Zusammenfassung: | 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. |
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DOI: | 10.48550/arxiv.2202.06052 |