Results 71 to 80 of about 8,669,620 (261)
CT-DQN: Control-Tutored Deep Reinforcement Learning [PDF]
, 2023 One of the major challenges in Deep Reinforcement Learning for control is the need for extensive training to learn a policy. Motivated by this, we present the design of the Control-Tutored Deep QNetworks (CT-DQN) algorithm, a Deep Reinforcement Learning ...
Francesco De Lellis +4 more
core
A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems
International Journal of Adaptive Control and Signal Processing, Volume 39, Issue 3, Page 566-581, March 2025.A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam +2 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.Geometry‐driven thermal behavior in wire‐arc additive manufacturing (WAAM) influences microstructural evolution during nonequilibrium solidification of a chemically complex Fe–Cr–Nb–W–Mo–C nanocomposite system. By comparing different deposits configurations, distinct entropy–cooling rate correlations, segregation, and carbide evolution are revealed ...
Blanca Palacios +5 more
wiley +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
Advanced Engineering Materials, EarlyView.A simplified thermoplastic pultrusion model is developed to predict thermal fields in glass fiber/polyethylene terephthalate (GF/PET) composites with reduced computational cost. By combining effective material homogenization, validation against literature data, and Gaussian‐process‐based optimization, the study reveals how heating limits, pulling speed,
Elder Soares +3 more
wiley +1 more source
Discrete-to-deep reinforcement learning methods
, 2022 Discrete-to-deep reinforcement learning ...
B Kurniawan (13420104) +4 more
core
Adaptive Foam 3D Printing of Ultralight and Multifunctional Materials
Advanced Engineering Materials, EarlyView.Adaptive foam 3D printing, enabled by expandable microspheres, imparts cellular structures to thermoplastic and thermosetting polymers, manufactured through a variety of processes including fused filament fabrication, direct ink writing, digital light processing, and inkjet printing.
Nariman Rajabifar, Amir Ameli
wiley +1 more source
Frontiers in Plant ScienceIntroductionThe rational structure of forest stands plays a crucial role in maintaining ecosystem functions, enhancing community stability, and ensuring sustainable management.
Jian Zhao +4 more
doaj +1 more source
Deep reinforcement learning in medicine
, 2019 Reinforcement learning has achieved tremendous success in recent years, notably in complex games such as Atari, Go, and chess. In large part, this success has been made possible by powerful function approximation methods in the form of deep neural ...
Jonsson, Anders, 1973-
core +1 more source
Applications of Deep Learning and Reinforcement Learning to Biological Data [PDF]
, 2018 Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine ...
Amir Hussain +15 more
core +1 more source