SwePub
Sök i LIBRIS databas

  Extended search

id:"swepub:oai:research.chalmers.se:6f91ddb5-484d-4cd7-9591-ddbc27f7b0ba"
 

Search: id:"swepub:oai:research.chalmers.se:6f91ddb5-484d-4cd7-9591-ddbc27f7b0ba" > Reinforcement Learn...

  • 1 of 1
  • Previous record
  • Next record
  •    To hitlist

Reinforcement Learning Informed by Optimal Control

Önnheim, Magnus, 1985 (author)
Göteborgs universitet,University of Gothenburg,Chalmers tekniska högskola,Chalmers University of Technology
Andersson, Pontus, 1995 (author)
Chalmers tekniska högskola,Chalmers University of Technology,Göteborgs universitet,University of Gothenburg
Gustavsson, Emil, 1987 (author)
Göteborgs universitet,University of Gothenburg,Chalmers tekniska högskola,Chalmers University of Technology
show more...
Jirstrand, Mats, 1968 (author)
Chalmers tekniska högskola,Chalmers University of Technology,Göteborgs universitet,University of Gothenburg
show less...
 (creator_code:org_t)
2019-09-09
2019
English.
In: Lecture Notes in Computer Science. - Cham : Springer International Publishing. - 0302-9743 .- 1611-3349. ; 11731, s. 403-407
  • Conference paper (peer-reviewed)
Abstract Subject headings
Close  
  • Model-free reinforcement learning has seen tremendous advances in the last few years, however practical applications of pure reinforcement learning are still limited by sample inefficiency and the difficulty of giving robustness and stability guarantees of the proposed agents. Given access to an expert policy, one can increase sample efficiency by in addition to learning from data, and also learn from the experts actions for safer learning. In this paper we pose the question whether expert learning can be accelerated and stabilized if given access to a family of experts which are designed according to optimal control principles, and more specifically, linear quadratic regulators. In particular we consider the nominal model of a system as part of the action space of a reinforcement learning agent. Further, using the nominal controller, we design customized reward functions for training a reinforcement learning agent, and perform ablation studies on a set of simple benchmark problems.

Subject headings

SAMHÄLLSVETENSKAP  -- Utbildningsvetenskap -- Lärande (hsv//swe)
SOCIAL SCIENCES  -- Educational Sciences -- Learning (hsv//eng)
TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Keyword

Adaptive control
Expert learning
Optimal control
Linear quadratic control
Reinforcement learning
Online learning

Publication and Content Type

kon (subject category)
ref (subject category)

Find in a library

To the university's database

  • 1 of 1
  • Previous record
  • Next record
  •    To hitlist

Search outside SwePub

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Close

Copy and save the link in order to return to this view