Results 31 to 40 of about 640,433 (264)

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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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, Susan Amin, Hossein Aboutalebi, Alexander Wong, Doina Precup   +4 more
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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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Atari games and Intel processors

, 2017
The asynchronous nature of the state-of-the-art reinforcement learning algorithms such as the Asynchronous Advantage Actor-Critic algorithm, makes them exceptionally suitable for CPU computations.
Adamski, Robert, Grel, Tomasz, Klimek, Maciej, Michalewski, Henryk   +3 more
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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, E. Susanne Blazek, PhD, Christopher T. Symons, PhD   +2 more
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Virtual to Real Reinforcement Learning for Autonomous Driving

, 2017
Reinforcement learning is considered as a promising direction for driving policy learning. However, training autonomous driving vehicle with reinforcement learning in real environment involves non-affordable trial-and-error. It is more desirable to first
Lu, Cewu, Pan, Xinlei, Wang, Ziyan, You, Yurong   +3 more
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Reinforcement Learning Approaches in Social Robotics

Sensors, 2021
This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior.
Neziha Akalin, Amy Loutfi
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Selection in Scale-Free Small World

, 2005
In this paper we compare the performance characteristics of our selection based learning algorithm for Web crawlers with the characteristics of the reinforcement learning algorithm. The task of the crawlers is to find new information on the Web.
Farkas, Cs., Lorincz, A., Palotai, Zs.
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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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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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