Results 91 to 100 of about 6,944,236 (307)

Deep Reinforcement Learning methods for StarCraft II Learning Environment [PDF]

, 2022
Reinforcement Learning (RL) is a Machine Learning framework in which an agent learns to solve a task by trial-and-error interaction with the surrounding environment.
Dainese, Nicola
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, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Comparative Study of Reinforcement Learning and Null-Space Projection-Based Control Framework for a High-DoF Manipulator for Automated Coating

Actuators
The coating process of a ship-hull interior requires automation owing to significant occupational hazards associated with its working environment. The interior of a ship hull is large and structurally complex, and hence requires a highly redundant ...
Yeonwoo Mo, Changhyun Cho, Jungmin Kim, Sejin Kim   +3 more
doaj   +1 more source

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, Nicholas Schaffer, Xiao Yang   +2 more
wiley   +1 more source

Evaluating reinforcement learning for game theory application learning to price airline seats under competition

, 2009
Applied Game Theory has been criticised for not being able to model real decision making situations. A game's sensitive nature and the difficultly in determining the utility payoff functions make it hard for a decision maker to rely upon any game ...
Collins, Andrew
core   +1 more source

Knowledge-guided reinforcement learning for self-adaptive software engineering and multi-objective quality optimization

Scientific Reports
The current software engineering field is experiencing growing pressures due to the complexity of software systems, rapidly changing requirements, and high-quality expectations in a dynamic environment.
Padmavathi V.
doaj   +1 more source

Temporal Trajectories of the Tau Aggregate Interactome Reveal Stage‐Specific Vulnerabilities in Alzheimer's Disease

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, Paula Beltran Lobo, Yunzhao Wu, Lyla A. Rowe, Emre Fertan, Cara L. Croft, Maria Jimenez‐Sanchez, Dezerae Cox, David Klenerman   +8 more
wiley   +1 more source

RL-ABC: Reinforcement learning for accelerator beamline control

Computer Physics Communications
Particle accelerator beamline optimization is a high-dimensional control problem traditionally requiring significant expert intervention. We present RLABC (Reinforcement Learning for Accelerator Beamline Control), an open-source Python framework that automatically transforms standard Elegant beamline configurations into reinforcement learning ...
Anwar Ibrahim, Fedor Ratnikov, Maxim Kaledin, Alexey Petrenko, Denis Derkach   +4 more
openaire   +2 more sources

Conformer-RL: A deep reinforcement learning library for conformer generation

, 2022
Conformer-RL is an open-source Python package for applying deep reinforcement learning (RL) to the task of generating a diverse set of low-energy conformations for a single molecule.
He, Yifei, Zimmerman, Paul M., Tewari, Ambuj, Jiang, Runxuan, Kammeraad, Joshua, Gogineni, Tarun   +5 more
core   +1 more source

Q‐LEAP: Millisecond Hyperdimensional Optimization for Full‐Spectrum Optical Metamaterials

Advanced Science, EarlyView.
Q‐LEAP integrates physics‐informed residual machine learning with factorization‐machine‐encoded quantum annealing to design full‐spectrum optical metamaterials. It explores a 2108 design space and, in a single 2.56 ms annealing step, reaches 85.83% of the theoretical FoM limit, enabling selective 5‐8 µm emission with 3–5 and 8–14 µm suppression and ∼40×
Zikang Guo, Yunwen Wu, Yuhui Guo, Shenghong Ju   +3 more
wiley   +1 more source