Results 31 to 40 of about 6,531,013 (293)

On the convergence of reinforcement learning [PDF]

Journal of Economic Theory, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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On the Use of Deep Reinforcement Learning for Visual Tracking: A Survey

IEEE Access, 2021
This paper aims at highlighting cutting-edge research results in the field of visual tracking by deep reinforcement learning. Deep reinforcement learning (DRL) is an emerging area combining recent progress in deep and reinforcement learning.
Giorgio Cruciata, Liliana Lo Presti, Marco La Cascia   +2 more
doaj   +1 more source

Curriculum Learning in Reinforcement Learning [PDF]

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017
Transfer learning in reinforcement learning is an area of research that seeks to speed up or improve learning of a complex target task, by leveraging knowledge from one or more source tasks. This thesis will extend the concept of transfer learning to curriculum learning, where the goal is to design a sequence of source tasks for an agent to train on ...
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Learning to Optimize for Reinforcement Learning

CoRR, 2023
Published at RLC 2024.
Qingfeng Lan, A. Rupam Mahmood, Shuicheng Yan, Zhongwen Xu   +3 more
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Emotional State and Feedback-Related Negativity Induced by Positive, Negative, and Combined Reinforcement

Frontiers in Psychology, 2021
Reinforcement learning relies on the reward prediction error (RPE) signals conveyed by the midbrain dopamine system. Previous studies showed that dopamine plays an important role in both positive and negative reinforcement.
Shuyuan Xu, Yuyan Sun, Min Huang, Yanhong Huang, Jing Han, Xuemei Tang, Wei Ren   +6 more
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Reinforcement Learning to Rank [PDF]

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 2019
Interactive systems such as search engines or recommender systems are increasingly moving away from single-turn exchanges with users. Instead, series of exchanges between the user and the system are becoming mainstream, especially when users have complex needs or when the system struggles to understand the user's intent.
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Fuzzy and tile coding approximation techniques for coevolution in reinforcement learning [PDF]

, 2005
PhDThis thesis investigates reinforcement learning algorithms suitable for learning in large state space problems and coevolution. In order to learn in large state spaces, the state space must be collapsed to a computationally feasible size and then ...
Tokarchuk, Laurissa Nadia
core   +4 more sources

Reinforcement Learning: A Survey

Journal of Artificial Intelligence Research, 1996
This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized.
Leslie Pack Kaelbling, Michael L. Littman, Andrew W. Moore 0001   +2 more
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Analysis and method comparsion of online and offline reinforcement learning [PDF]

ITM Web of Conferences
In this paper, an exploration of the online and offline precepts of reinforcement learning and the associated algorithms of paradigms is carried out in a systematic manner.
Zheng Changhang
doaj   +1 more source

Review of Attention Mechanisms in Reinforcement Learning [PDF]

Jisuanji kexue yu tansuo
In recent years, the combination of reinforcement learning and attention mechanisms has attracted an increasing attention in algorithmic research field.
XIA Qingfeng, XU Ke'er, LI Mingyang, HU Kai, SONG Lipeng, SONG Zhiqiang, SUN Ning
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