Reinforcement Learning Based on Real-Time Iteration NMPC

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00350533" target="_blank" >RIV/68407700:21230/20:00350533 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.ifacol.2020.12.1195" target="_blank" >https://doi.org/10.1016/j.ifacol.2020.12.1195</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ifacol.2020.12.1195" target="_blank" >10.1016/j.ifacol.2020.12.1195</a>

Result language

angličtina

Original language name

Reinforcement Learning Based on Real-Time Iteration NMPC

Original language description

Reinforcement Learning (RL) has proven a stunning ability to learn optimal policies from data without any prior knowledge on the process. The main drawback of RL is that it is typically very difficult to guarantee stability and safety. On the other hand, Nonlinear Model Predictive Control (NMPC) is an advanced model-based control technique which does guarantee safety and stability, but only yields optimality for the nominal model. Therefore, it has been recently proposed to use NMPC as a function approximator within RL. While the ability of this approach to yield good performance has been demonstrated, the main drawback hindering its applicability is related to the computational burden of NMPC, which has to be solved to full convergence. In practice, however, computationally efficient algorithms such as the Real-Time Iteration (RTI) scheme are deployed in order to return an approximate NMPC solution in very short time. In this paper we bridge this gap by extending the existing theoretical framework to also cover RL based on RTI NMPC. We demonstrate the effectiveness of this new RL approach with a nontrivial example modeling a challenging nonlinear system subject to stochastic perturbations with the objective of optimizing an economic cost.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the IFAC World Congress 2020

ISBN

—

ISSN

2405-8963

e-ISSN

2405-8963

Number of pages

6

Pages from-to

5213-5218

Publisher name

IFAC

Place of publication

Laxenburg

Event location

Berlín

Event date

Jul 11, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000652593000142