Results 91 to 100 of about 6,522,305 (296)
Refined Risk Management in Safe Reinforcement Learning with a Distributional Safety Critic
, 2022 Safety is critical to broadening the real-world use of reinforcement learning (RL). Modeling the safety aspects using a safety-cost signal separate from the reward is becoming standard practice, since it avoids the problem of finding a good balance ...
Yang, Q. (author) +3 more
core
Sustainable Materials Design With Multi‐Modal Artificial Intelligence
Advanced Science, EarlyView.Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu +8 more
wiley +1 more source
Advanced Science, EarlyView.An artificial bifunctional N‐deacetylase/N‐sulfotransferase was engineered in Escherichia coli by combining screened N‐deacetylases with an NST domain. The integrated engineering strategy improved enzyme performance. The optimized enzyme efficiently converted heparosan into N‐sulfated heparosan, addressing a key bottleneck in microbial heparin ...
Xintong Xi +8 more
wiley +1 more source
Learning Strict Nash Equilibria through Reinforcement [PDF]
This paper studies the analytical properties of the reinforcement learning model proposed in Erev and Roth (1998), also termed cumulative reinforcement learning in Laslier et al (2001).
Ianni, Antonella
core
Neuromorphic Devices and Computing for Sensing, Memory, and Control
Advanced Science, EarlyView.This review introduces neuromorphic devices made from diverse materials. These devices mimic neuronal functions and architectures and, when integrated with artificial or biological computing, can form closed loops with neurons for pressure, optical, acoustic, and biochemical sensing and modulation.
Zhengguang Zhu +2 more
wiley +1 more source
Adaptive image augmentation using reinforcement learning and neural style transfer for corn disease diagnosis [PDF]
PeerJ Computer ScienceThis study proposed an Integrated Reinforcement Learning–Neural Style Transfer (RL-NST) framework for image augmentation that improves corn leaf disease diagnosis. It uses a hybrid dataset comprising field-acquired and publicly available corn leaf images
Kanchanadevi K., Sandhia G. K.
doaj +2 more sources
Advanced Science, EarlyView.Recent advances in metasurface‐enabled low‐observable technologies are reviewed from the perspective of cross‐scale material–structure synergy. Electromagnetic, thermal, optical, and acoustic stealth are highlighted together with dynamic tuning, programmable coding, data‐driven inverse design, artificial intelligence, multispectral compatibility, and ...
Shuhao Wang +5 more
wiley +1 more source
Automatic composition of Guzheng (Chinese Zither) music using long short-term memory network (LSTM) and reinforcement learning (RL). [PDF]
Sci Rep, 2022 Chen S, Zhong Y, Du R.
europepmc +1 more source
A Comparative Analysis of Reinforcement Learning Methods
, 1991 This paper analyzes the suitability of reinforcement learning (RL) for both programming and adapting situated agents. We discuss two RL algorithms: Q-learning and the Bucket Brigade.
Mataric, Maja
core
Advanced Science, EarlyView.Tau populations with different aggregation states associate with distinct sets of proteins in the Alzheimer's disease brain. Proteomic discovery combined with single‐molecule analysis in neurons reveals dynamic associations involving proteostasis, metabolism and RNA biology.
Dorothea Böken +8 more
wiley +1 more source