Results 141 to 150 of about 198,528 (317)

Fairness in Reinforcement Learning

, 2016
We initiate the study of fairness in reinforcement learning, where the actions of a learning algorithm may affect its environment and future rewards. Our fairness constraint requires that an algorithm never prefers one action over another if the long-term (discounted) reward of choosing the latter action is higher. Our first result is negative: despite
Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth 0001   +4 more
openaire   +3 more sources

Direct policy search reinforcement learning based on particle filtering [PDF]

, 2012
We reveal a link between particle filtering methods and direct policy search reinforcement learning, and propose a novel reinforcement learning algorithm, based heavily on ideas borrowed from particle filters.
Caldwell, Darwin G, Kormushev, Petar
core  

Engineered AuNPs/fMWCNT Nanocomposite Electrodes for High‐Sensitivity Methylglyoxal Sensing in Saliva and Sweat for Non‐Invasive Diabetes Monitoring

Advanced Healthcare Materials, EarlyView.
An AuNPs/fMWCNT nanocomposite‐modified screen‐printed carbon electrode was engineered via sequential electrodeposition and integrated into a 3D‐printed microfluidic platform for ultrasensitive methylglyoxal detection. The non‐invasive sensing platform enables rapid analysis in saliva and sweat, highlighting strong potential for wearable point‐of‐care ...
Ahadul Amin Soshi, Md Younus Ali, Dadbeh Pazuki, Matiar M.R. Howlader   +3 more
wiley   +1 more source

Reinforcement learning

Scholarpedia, 2008
Florentin Wörgötter, Bernd Porr
openaire   +1 more source

Reinforcement Learning for Blackjack [PDF]

, 2009
This paper explores the development of an Artificial Intelligence system for an already existing framework of card games, called SKCards, and the experimental results obtained from this. The current Artificial intelligence in the SKCards Blackjack is highly flawed. Reinforcement Learning was chosen as the method to be employed.
openaire   +1 more source

Artificial Intelligence‐Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling

Advanced Materials, EarlyView.
AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll, Ivan Pinto‐Huguet, Enzo Rotunno, Thomas Galvani, Catalina Coll, Payam Habibzadeh Kavkani, Maria Chiara Spadaro, Yann‐Michel Niquet, Martin Børstad Eriksen, Sara Martí‐Sánchez, Georgios Katsaros, Giordano Scappucci, Peter Krogstrup, Giovanni Isella, Andreu Cabot, Gonzalo Merino, Pablo Ordejón, Stephan Roche, Vincenzo Grillo, Jordi Arbiol   +19 more
wiley   +1 more source

Learning graph based individual intrinsic reward for multi-agent reinforcement learning

ICT Express
Designing a reward function is a critical challenge in reinforcement learning. However, as environments become more complex and tasks grow more difficult, designing a reward function that drives optimal behavior becomes increasingly challenging.
Seokhun Ju, Seungyub Han, Taehyun Cho, Jungwoo Lee, Taeyoung Lee, Minkyoung Kim, Jinho An   +6 more
doaj   +1 more source

Does eeasoning enhance learning? [PDF]

Utilizing the well-known Ultimatum Game, this note presents the following phenomenon. If we start with simple stimulus-response agents, learning through naive reinforcement, and then grant them some introspective capabilities, we get outcomes that are ...
Nicolaas J. Vriend
core  

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

Topology and Material Optimization in Ultra‐Soft Magneto‐Active Structures: Making Advantage of Residual Anisotropies

Advanced Materials, EarlyView.
Residual magnetization induces pronounced mechanical anisotropy in ultra‐soft magnetorheological elastomers, shaping deformation and actuation even without external magnetic fields. This study introduces a computational‐experimental framework integrating magneto‐mechanical coupling into topology optimization for designing soft magnetic actuators with ...
Carlos Perez‐Garcia, Rogelio Ortigosa, Jesús Martínez‐Frutos, Daniel Garcia‐Gonzalez   +3 more
wiley   +1 more source