Results 71 to 80 of about 685,298 (326)
Advanced Materials Interfaces, EarlyView., 2023 This review discusses the use of Surface‐Enhanced Raman Spectroscopy (SERS) combined with Artificial Intelligence (AI) for detecting antimicrobial resistance (AMR). Various SERS studies used with AI techniques, including machine learning and deep learning, are analyzed for their advantages and limitations.
Zakarya Al‐Shaebi +4 more
wiley +1 more source
物联网学报, 2023 Deep reinforcement learning has strong abilities of decision-making and generalization and often applies to the quality of service (QoS) optimization in software defined network (SDN).However, traditional deep reinforcement learning algorithms have ...
Cenhuishan LIAO +4 more
doaj +2 more sources
IEEE Access, 2020 The motivation behind our work is to review and analyze the most relevant studies on deep reinforcement learning-based object manipulation. Various studies are examined through a survey of existing literature and investigation of various aspects, namely,
Marwan Qaid Mohammed +2 more
doaj +1 more source
Multi-Agent Deep Reinforcement Learning with Human Strategies
, 2019 Deep learning has enabled traditional reinforcement learning methods to deal with high-dimensional problems. However, one of the disadvantages of deep reinforcement learning methods is the limited exploration capacity of learning agents.
Nahavandi, Saeid +2 more
core +1 more source
Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers [PDF]
arXiv, 2021 In this paper, we propose a new data poisoning attack and apply it to deep reinforcement learning agents. Our attack centers on what we call in-distribution triggers, which are triggers native to the data distributions the model will be trained on and deployed in.
arxiv
A review of artificial intelligence in brachytherapy
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Artificial intelligence (AI) has the potential to revolutionize brachytherapy's clinical workflow. This review comprehensively examines the application of AI, focusing on machine learning and deep learning, in various aspects of brachytherapy.
Jingchu Chen +4 more
wiley +1 more source
Multiagent Deep Reinforcement Learning Algorithms in StarCraft II: A Review
IEEE AccessStarCraft II, as a real-time strategy game, features multiagent collaboration, complex decision-making processes, partially observable environments, and long-term credit assignment; thus, it is an ideal platform for exploring, validating, and optimizing ...
Yanyan Li, Yijun Wang, Yiwei Zhou
doaj +1 more source
Deep learning, reinforcement learning, and world models
Neural Networks, 2022 Deep learning (DL) and reinforcement learning (RL) methods seem to be a part of indispensable factors to achieve human-level or super-human AI systems. On the other hand, both DL and RL have strong connections with our brain functions and with neuroscientific findings.
Yutaka Matsuo +7 more
openaire +3 more sources
Methods for working with problem residents in medical physics residency education
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Medical physics residency training programs may occasionally encounter residents requiring additional intervention beyond normal training efforts. In the literature, these residents are referred to as “problem” residents. While the physician literature on the subject is valuable, this paper specifically focuses on dealing with a problem ...
Christopher J. Watchman, Dandan Zheng
wiley +1 more source
Reinforcement Learning: Theory and Applications in HEMS
Energies, 2022 The steep rise in reinforcement learning (RL) in various applications in energy as well as the penetration of home automation in recent years are the motivation for this article.
Omar Al-Ani, Sanjoy Das
doaj +1 more source