Results 151 to 160 of about 34,803,504 (287)

Knowledge and Ignorance in Reinforcement Learning [PDF]

, 2011
The field of Reinforcement Learning is concerned with teaching agents to take optimal decisions to maximize their total utility in complicated environments. A Reinforcement Learning problem, generally described by the Markov Decision Process formalism,
Ramachandran, Deepak
core  

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

Silicone Breast Implants as a Model System for Understanding Polymer Permeation in Soft Materials In Vivo

Advanced Healthcare Materials, EarlyView.
Silicone breast implants are presented as a model system for understanding polymer permeation in vivo. Rather than representing material failure, “gel bleed” emerges from solution–diffusion transport‐mediated. By integrating polymer architecture, physicochemical transport, and biointerfacial processes, this review provides a unified framework that ...
D. Bouyer, H. Mutlu, C. Charmette, E. Randrianaridera, Z. Adaidi, R. Hedna, L. Pieuchot, I. Pluvy, K. Anselme, N. Couturier, J. P. Mericq, A. Ponche, I. Brigaud   +12 more
wiley   +1 more source

Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H2O2 Production via Data Science

Advanced Materials, EarlyView.
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu, Hsi‐wen Wu, Xiao Wang, Jie Zhang   +3 more
wiley   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

Multi-agent reinforcement learning for planning and scheduling multiple goals

, 2000
Recently, reinforcement learning has been proposed as an effective method for knowledge acquisition of the multiagent systems. However, most researches on multiagent system applying a reinforcement learning algorithm focus on the method to reduce ...
Arai, Sachiyo, Sycara, Katia, Payne, Terry R.   +2 more
core   +1 more source

Azobenzene's Cross‐Scale Optics and Photonics: Molecular Photoswitching, Mesoscopic Material Motions, and Adaptive Devices

Advanced Materials, EarlyView.
Azobenzene photoswitches translate molecular‐scale E/Z photoisomerization into macroscopic material responses and device‐level photonic functions. This Review highlights how azobenzene research has evolved from molecular photochemistry to photoalignment, mass migration, photomechanics, and heat release, ultimately enabling holography, reconfigurable ...
Heeju Son, Soyul Kwak, Heerin Noh, Minsang Kim, Doyeon Noh, Subhayan Chakraborty, Jaewon Lee, YongDeok Cho, Kyeongsoo Kim, Taewoong Eom, Moonyoung Kim, Jangwon Kim, Hoyoung Lee, Soyoung Lee, Jaehun Kim, Grace G. D. Han, Shin‐Tson Wu, Stefano Oscurato, Arri Priimagi, Marina Saphiannikova, Seungwoo Lee   +20 more
wiley   +1 more source

Non-Stationary Markov Decision Processes a Worst-Case Approach using Model-Based Reinforcement Learning [PDF]

, 2019
This work tackles the problem of robust zero-shot planning in non-stationary stochastic environments. We study Markov Decision Processes (MDPs) evolving over time and consider Model-Based Reinforcement Learning algorithms in this setting.
Lecarpentier, Erwan, Rachelson, Emmanuel
core  

Thermodynamic Limits to Molecular Doping in Conjugated Polymers: A Perspective on Phase Behavior and Miscibility

Advanced Materials, EarlyView.
Molecular doping of conjugated polymers is fundamentally constrained by thermodynamic phase behavior. This Perspective reframes doping efficiency and stability in terms of miscibility limits, binodals, and solvus boundaries, highlighting the role of effective interaction parameters and charge transfer.
Somayeh Kashani, Justin Neu, Sung‐Joo Kwon, Runqio Song, Zixuan Chen, Tajah Trapier, Paschalis Gkoupidenis, David Ginger, Wei You, Jasper J. Michels, Harald Ade   +10 more
wiley   +1 more source

Optimal Control Drives Ultrafast and Energy‐Efficient Magnetization Switching in Van der Waals Magnets

Advanced Materials, EarlyView.
ABSTRACT The accelerating expansion of data‐centric technologies is sharply increasing the energy burden of information storage, placing unprecedented pressure on the efficiency of magnetic switching. Conventional field‐driven reversal, once the foundation of magnetic memory, has become impractical in modern architectures due to its high energy cost ...
Mohammad H. Badarneh, PeiYu Cai, Elton J. G. Santos   +2 more
wiley   +1 more source