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

TL;DR

This paper explores integrating causality, control, and reinforcement learning to actively manipulate dynamical systems, aiming to improve control strategies through combined insights and competition-based evaluation.

Contribution

It introduces a novel competition framework that combines observational and interventional data to advance control methods using causality and reinforcement learning.

Findings

Open-loop impulse control strategies identified

Closed-loop control policies optimized for system states

Open-sourced code facilitates further research and method testing

Abstract

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.