Bo Bernhardsson
Modellering och styrning av osäkra system. Programdirektör för masterprogrammet i maskininlärning, system och styrning.
Dual Control by Reinforcement Learning Using Deep Hyperstate Transition Models
Författare
Summary, in English
In dual control, the manipulated variables are used to both regulate the system and identify unknown parameters. The joint probability distribution of the system state and the parameters is known as the hyperstate. The paper proposes a method to perform dual control using a deep reinforcement learning algorithm in combination with a neural network model trained to represent hyperstate transitions. The hyperstate is compactly represented as the parameters of a mixture model that is fitted to Monte Carlo samples of the hyperstate. The representation is used to train a hyperstate transition model, which is used by a standard reinforcement learning algorithm to find a dual control policy. The method is evaluated on a simple nonlinear system, which illustrates a situation where probing is needed, but it can also scale to high-dimensional systems. The method is demonstrated to be able to learn a probing technique that reduces the uncertainty of the hyperstate, resulting in improved control performance.
Avdelning/ar
- Institutionen för reglerteknik
- LTH profilområde: AI och digitalisering
- LTH profilområde: Teknik för hälsa
Publiceringsår
2022
Språk
Engelska
Sidor
395-401
Publikation/Tidskrift/Serie
IFAC-PapersOnLine
Volym
55
Dokumenttyp
Konferensbidrag
Ämne
- Control Engineering
Nyckelord
- adaptive control
- adaptive control by neural networks
- Bayesian methods
- nonlinear adaptive control
- reinforcement learning control
- stochastic optimal control problems
Conference name
14th IFAC Workshop on Adaptive and Learning Control Systems, ALCOS 2022
Conference date
2022-06-29 - 2022-07-01
Conference place
Casablanca, Morocco
Status
Published
Projekt
- Efficient Learning of Dynamical Systems
ISBN/ISSN/Övrigt
- ISSN: 2405-8963