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한승열

Han, Seungyul
Machine Learning & Intelligent Control Lab.
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dc.citation.conferencePlace US -
dc.citation.conferencePlace Virtual-only -
dc.citation.title Neural Information Processing Systems -
dc.contributor.author Han, Seungyul -
dc.contributor.author Sung, Youngchul -
dc.date.accessioned 2024-01-31T21:06:24Z -
dc.date.available 2024-01-31T21:06:24Z -
dc.date.created 2021-11-28 -
dc.date.issued 2021-12-08 -
dc.description.abstract In this paper, we propose a max-min entropy framework for reinforcement learning (RL) to overcome the limitation of the soft actor-critic (SAC) algorithm implementing the maximum entropy RL in model-free sample-based learning. Whereas the maximum entropy RL guides learning for policies to reach states with high entropy in the future, the proposed max-min entropy framework aims to learn to visit states with low entropy and maximize the entropy of these low-entropy states to promote better exploration. For general Markov decision processes (MDPs), an efficient algorithm is constructed under the proposed max-min entropy framework based on disentanglement of exploration and exploitation. Numerical results show that the proposed algorithm yields drastic performance improvement over the current state-of-the-art RL algorithms. -
dc.identifier.bibliographicCitation Neural Information Processing Systems -
dc.identifier.scopusid 2-s2.0-85126629617 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/76467 -
dc.identifier.url https://papers.nips.cc/paper/2021/hash/d7b76edf790923bf7177f7ebba5978df-Abstract.html -
dc.publisher Neural Information Processing Systems -
dc.title A Max-Min Entropy Framework for Reinforcement Learning -
dc.type Conference Paper -
dc.date.conferenceDate 2021-12-06 -

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