Reinforcement Learning in System Identification
Abstract
System identification, also known as learning forward models, transfer functions, system dynamics, etc., has a long tradition both in science and engineering in different fields. Particularly, it is a recurring theme in Reinforcement Learning research, where forward models approximate the state transition function of a Markov Decision Process by learning a mapping function from current state and action to the next state. This problem is commonly defined as a Supervised Learning problem in a direct way. This common approach faces several difficulties due to the inherent complexities of the dynamics to learn, for example, delayed effects, high non-linearity, non-stationarity, partial observability and, more important, error accumulation when using bootstrapped predictions (predictions based on past predictions), over large time horizons. Here we explore the use of Reinforcement Learning in this problem. We elaborate on why and how this problem fits naturally and sound as a Reinforcement Learning problem, and present some experimental results that demonstrate RL is a promising technique to solve these kind of problems.
Cite
@article{arxiv.2212.07123,
title = {Reinforcement Learning in System Identification},
author = {Jose Antonio Martin H. and Oscar Fernandez Vicente and Sergio Perez and Anas Belfadil and Cristina Ibanez-Llano and Freddy Jose Perozo Rondon and Jose Javier Valle and Javier Arechalde Pelaz},
journal= {arXiv preprint arXiv:2212.07123},
year = {2022}
}
Comments
Accepted in Neurips Deep Reinforcement Learning Workshop 2022: https://openreview.net/forum?id=fGcbpWQIJZV