影响稀疏奖励领域深度强化学习的环境特征概述

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jim Martin Catacora Ocana, R. Capobianco, D. Nardi
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引用次数: 1

摘要

近年来,深度强化学习取得了令人印象深刻的成果;然而,它仍然受到奖励稀少的环境的严重困扰。最重要的是,并不是所有的稀疏奖励环境都是平等的,也就是说,它们可能因存在或不存在各种特征而有所不同,其中许多特征对学习有很大的影响。鉴于此,本工作将这些环境特征的文献汇编放在一起,特别是那些已经被利用的和那些继续构成挑战的环境特征。我们希望这项工作能够为研究人员提供指导,以评估他们的新建议的普遍性,并提请他们注意在处理稀疏奖励时仍未解决的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Overview of Environmental Features that Impact Deep Reinforcement Learning in Sparse-Reward Domains
Deep reinforcement learning has achieved impressive results in recent years; yet, it is still severely troubled by environments showcasing sparse rewards. On top of that, not all sparse-reward environments are created equal, i.e., they can differ in the presence or absence of various features, with many of them having a great impact on learning. In light of this, the present work puts together a literature compilation of such environmental features, covering particularly those that have been taken advantage of and those that continue to pose a challenge. We expect this effort to provide guidance to researchers for assessing the generality of their new proposals and to call their attention to issues that remain unresolved when dealing with sparse rewards.
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来源期刊
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research 工程技术-计算机:人工智能
CiteScore
9.60
自引率
4.00%
发文量
98
审稿时长
4 months
期刊介绍: JAIR(ISSN 1076 - 9757) covers all areas of artificial intelligence (AI), publishing refereed research articles, survey articles, and technical notes. Established in 1993 as one of the first electronic scientific journals, JAIR is indexed by INSPEC, Science Citation Index, and MathSciNet. JAIR reviews papers within approximately three months of submission and publishes accepted articles on the internet immediately upon receiving the final versions. JAIR articles are published for free distribution on the internet by the AI Access Foundation, and for purchase in bound volumes by AAAI Press.
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