Results 61 to 70 of about 685,298 (326)
, 2017 We describe a method to use discrete human feedback to enhance the performance of deep learning agents in virtual three-dimensional environments by extending deep-reinforcement learning to model the confidence and consistency of human feedback.
Ashley Haines (4342138) +14 more
core +2 more sources
Bayesian Deep Reinforcement Learning via Deep Kernel Learning
International Journal of Computational Intelligence Systems, 2018 Reinforcement learning (RL) aims to resolve the sequential decision-making under uncertainty problem where an agent needs to interact with an unknown environment with the expectation of optimising the cumulative long-term reward. Many real-world problems
Junyu Xuan +3 more
doaj +1 more source
Learning Mobile Manipulation through Deep Reinforcement Learning
Sensors, 2020 Mobile manipulation has a broad range of applications in robotics. However, it is usually more challenging than fixed-base manipulation due to the complex coordination of a mobile base and a manipulator.
Cong Wang +7 more
doaj +1 more source
Molecular Oncology, EarlyView.This article advocates integrating temporal dynamics into cancer research. Rather than relying on static snapshots, researchers should increasingly consider adopting dynamic methods—such as live imaging, temporal omics, and liquid biopsies—to track how tumors evolve over time.
Gautier Follain +3 more
wiley +1 more source
Aero-Engine Modeling and Control Method with Model-Based Deep Reinforcement Learning
Aerospace, 2023 Due to the strong representation ability and capability of learning from data measurements, deep reinforcement learning has emerged as a powerful control method, especially for nonlinear systems, such as the aero-engine control system.
Wenbo Gao +4 more
doaj +1 more source
FEBS Open Bio, EarlyView.This review highlights how foundation models enhance predictive healthcare by integrating advanced digital twin modeling with multiomics and biomedical data. This approach supports disease management, risk assessment, and personalized medicine, with the goal of optimizing health outcomes through adaptive, interpretable digital simulations, accessible ...
Sakhaa Alsaedi +2 more
wiley +1 more source
, 2018 Under review for Morgan & Claypool: Synthesis Lectures in Artificial Intelligence and Machine ...
openaire +4 more sources
Real-time security margin control using deep reinforcement learning
Energy and AI, 2023 This paper develops a real-time control method based on deep reinforcement learning aimed to determine the optimal control actions to maintain a sufficient secure operating limit.
Hannes Hagmar +2 more
doaj
An FPGA-Based On-Device Reinforcement Learning Approach using Online Sequential Learning
, 2021 DQN (Deep Q-Network) is a method to perform Q-learning for reinforcement learning using deep neural networks. DQNs require a large buffer and batch processing for an experience replay and rely on a backpropagation based iterative optimization, making ...
Matsutani, Hiroki +2 more
core
FEBS Open Bio, EarlyView.The tumor microenvironment is a dynamic, multifaceted complex system of interdependent cellular, biochemical, and biophysical components. Three‐dimensional in vitro models of the tumor microenvironment enable a better understanding of these interactions and their impact on cancer progression and therapeutic resistance.
Salma T. Rafik +3 more
wiley +1 more source