A Novel State Space Exploration Method for the Sparse-Reward Reinforcement Learning Environment

Liu, X. orcid.org/0009-0000-7674-3183, Ma, L. orcid.org/0009-0006-8776-9112, Chen, Z. orcid.org/0009-0002-2151-7028 et al. (4 more authors) (2023) A Novel State Space Exploration Method for the Sparse-Reward Reinforcement Learning Environment. In: 43rd SGAI International Conference on Artificial Intelligence, AI 2023, 12-14 Dec 2023, Cambridge, UK.

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Item Type: Conference or Workshop Item
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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is an author produced version of a conference paper accepted for publication in Artificial Intelligence XL (SGAI 2023). Uploaded in accordance with the publisher's self-archiving policy.

Keywords: Sparse-reward; Replay Sub-buffers; DQN; Exploration; Reinforcement Learning
Dates:
  • Published: 8 November 2023
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Medical and Biological Engineering (iMBE) (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 16 Apr 2024 14:02
Last Modified: 16 Apr 2024 14:02
Status: Published
Publisher: Springer Nature Switzerland
Identification Number: 10.1007/978-3-031-47994-6_18
Open Archives Initiative ID (OAI ID):

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