(The) meta reinforcement learning with multiple models = 다중 모델을 이용한 메타 강화 학습

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Most deep reinforcement learning algorithms are sample inefficient in complex and rich environments, so they need a large amount of sample to adapt to a new task. However, in the real world, adapting a new task quickly with a small amount of sample is essential. One way to solve this problem is the meta-learning that learns how to learn, and studies on meta-learning have been performed. However, prior meta-learning methods only consider the one model for adapting a new task, but having the only model for adaptation is not enough for more complex tasks. In this work, we propose a meta-learning method with multiple models for adapting to a new task in reinforcement learning (meta-RL). The proposed meta-RL algorithm is evaluated on a variety of locomotion tasks, and we show that the proposed algorithm is more effective at learning a new task.
Advisors
Sung, Youngchulresearcher성영철researcher
Description
한국과학기술원 :전기및전자공학부,
Publisher
한국과학기술원
Issue Date
2019
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2019.2,[iv, 20 p. :]

Keywords

Reinforcement learning▼ameta learning; 강화 학습▼a메타 러닝

URI
http://hdl.handle.net/10203/266817
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=843428&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
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