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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

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Exploration in deep reinforcement learning: A survey

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dc.contributor.author Ladosz, Pawel ko
dc.contributor.author Weng, Lilian ko
dc.contributor.author Kim, Minwoo ko
dc.contributor.author Oh, Hyondong ko
dc.date.available 2022-06-03T01:28:36Z -
dc.date.created 2022-05-26 ko
dc.date.issued 2022-09 ko
dc.identifier.citation INFORMATION FUSION, v.85, pp.1 - 22 ko
dc.identifier.issn 1566-2535 ko
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/58575 -
dc.description.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. ko
dc.language 영어 ko
dc.publisher ELSEVIER ko
dc.title Exploration in deep reinforcement learning: A survey ko
dc.type ARTICLE ko
dc.identifier.scopusid 2-s2.0-85128759155 ko
dc.identifier.wosid 000794853400001 ko
dc.type.rims ART ko
dc.identifier.doi 10.1016/j.inffus.2022.03.003 ko
dc.identifier.url https://www.sciencedirect.com/science/article/pii/S1566253522000288?via%3Dihub ko
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