BROWSE

Related Researcher

Author's Photo

Oh, Hyondong
Autonomous Systems Laboratory
Research Interests
  • Autonomy and decision making for unmanned vehicles
  • Cooperative control and path planning for unmanned vehicles
  • Nonlinear guidance and control
  • Estimation and sensor/information fusion
  • Vision-based navigation and control
  • Bio-inspired self-organising multi-vehicle system

ITEM VIEW & DOWNLOAD

Exploration in deep reinforcement learning: A survey

Cited 0 times inthomson ciCited 0 times inthomson ci
Title
Exploration in deep reinforcement learning: A survey
Author
Ladosz, PawelWeng, LilianKim, MinwooOh, Hyondong
Issue Date
2022-09
Publisher
ELSEVIER
Citation
INFORMATION FUSION, v.85, pp.1 - 22
Abstract
This paper reviews exploration techniques in deep reinforcement learning. Exploration techniques are of primary importance when solving sparse reward problems. In sparse reward problems, the reward is rare, which means that the agent will not find the reward often by acting randomly. In such a scenario, it is challenging for reinforcement learning to learn rewards and actions association. Thus more sophisticated exploration methods need to be devised. This review provides a comprehensive overview of existing exploration approaches, which are categorised based on the key contributions as: reward novel states, reward diverse behaviours, goal-based methods, probabilistic methods, imitation-based methods, safe exploration and random-based methods. Then, unsolved challenges are discussed to provide valuable future research directions. Finally, the approaches of different categories are compared in terms of complexity, computational effort and overall performance.
URI
https://scholarworks.unist.ac.kr/handle/201301/58575
URL
https://www.sciencedirect.com/science/article/pii/S1566253522000288?via%3Dihub
DOI
10.1016/j.inffus.2022.03.003
ISSN
1566-2535
Appears in Collections:
MEN_Journal Papers
Files in This Item:
There are no files associated with this item.

find_unist can give you direct access to the published full text of this article. (UNISTARs only)

Show full item record

qrcode

  • mendeley

    citeulike

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

MENU