Reinforcement learning - Open Access .click

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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 +6 more
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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 +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
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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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How are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review

Mayo Clinic Proceedings: Digital Health
To assess the current real-world applications of machine learning (ML) and artificial intelligence (AI) as functionality of digital behavior change interventions (DBCIs) that influence patient or consumer health behaviors.
Amy Bucher, PhD +2 more
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Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning

IEEE Access, 2023
While significant research advances have been made in the field of deep reinforcement learning, there have been no concrete adversarial attack strategies in literature tailored for studying the vulnerability of deep reinforcement learning algorithms to ...
Maziar Gomrokchi +4 more
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Sample Efficient Reinforcement Learning with REINFORCE

Proceedings of the AAAI Conference on Artificial Intelligence, 2021
Policy gradient methods are among the most effective methods for large-scale reinforcement learning, and their empirical success has prompted several works that develop the foundation of their global convergence theory. However, prior works have either required exact gradients or state-action visitation measure based mini-batch stochastic gradients ...
Junzi Zhang +3 more
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Adaptive Control with Approximated Policy Search Approach

ITB Journal of Engineering Science, 2010
Most of existing adaptive control schemes are designed to minimize error between plant state and goal state despite the fact that executing actions that are predicted to result in smaller errors only can mislead to non-goal states. We develop an adaptive
Agus Naba
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Comfort or hesitancy: A cross-sectional study of modifiable factors associated with co-vaccination behavior among United States and Canadian adults

Preventive Medicine Reports
Objective: Co-vaccination, or receiving multiple vaccines at once, may improve vaccination uptake and reduce missed opportunities to vaccinate. Although generally considered safe and effective, co-vaccination is not well accepted outside of travel and ...
Emily Frith +3 more
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A survey of benchmarks for reinforcement learning algorithms

South African Computer Journal, 2020
Reinforcement learning has recently experienced increased prominence in the machine learning community. There are many approaches to solving reinforcement learning problems with new techniques developed constantly.
Belinda Stapelberg, Katherine Mary Malan
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