Tech Xplore

Reinforcement learning accelerates model-free training of optical AI systems

Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive ...
Frontiers

An adaptive quasi-PR controller for modular multilevel converters based on deep reinforcement learning

1 Electric Power Research Institute, China Southern Power Grid, Guangzhou, China 2 Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Guangzhou, China In modern power systems, Modular ...
GitHub

Reinforcement learning in portfolio management

Motivated by "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem" by Jiang et. al. 2017 [1]. In this project: Implement three state-of-art continous deep ...
www.cs.utexas.edu

Hyperspherical Normalization for Scalable Deep Reinforcement Learning

@InProceedings{pstone_simba, author = {Hojoon Lee and Youngdo Lee and Takuma Seno and Donghu Kim and Peter Stone and Jaegul Choo}, title = {Hyperspherical Normalization for Scalable Deep Reinforcement ...
Devdiscourse

Sophisticated AI promising but less reliable for monetary policy decisions

The study, titled Reinforcement Learning for Monetary Policy Under Macroeconomic Uncertainty: Analyzing Tabular and Function ...
marktechpost

A New MIT Study Shows Reinforcement Learning Minimizes Catastrophic Forgetting Compared to Supervised Fine-Tuning

What is catastrophic forgetting in foundation models? Foundation models excel in diverse domains but are largely static once deployed. Fine-tuning on new tasks often introduces catastrophic forgetting ...
Forbes

Will Reinforcement Learning Take Us To AGI?

Nearly a century ago, psychologist B.F. Skinner pioneered a controversial school of thought, behaviorism, to explain human and animal behavior. Behaviorism directly inspired modern reinforcement ...
IEEE

An Analytic Policy Gradient-Based Deep Reinforcement Learning Motion Cueing Algorithm for Driving Simulators

Abstract: The proposed motion cueing algorithm (MCA), based on a reinforcement learning algorithm using gradient information to directly update the control policy, introduces three significant ...