Aerospace and Mechanical Insider on MSN

AI reinforcement learning tackles fusion plasma instabilities

The DIII-D National Fusion Facility in San Diego, operated by General Atomics, houses the largest and most advanced magnetic ...
Education Week

With Peter DeWitt & Michael Nelson

A false premise is an incorrect or flawed assumption that forms the basis of an argument or reasoning. When the starting point is wrong, the conclusion or action that follows is usually unsound or ...
CoinTelegraph

AI agent attempts unauthorized crypto mining during training, researchers say

The experimental AI agent ROME attempted to divert GPU resources for crypto mining during training and opened an external SSH tunnel, researchers said. A research team behind an autonomous AI agent ...
IEEE

A Deep Reinforcement Learning Framework Assisted by Genetic Programming for Dynamic Flexible Job Shop Scheduling

Abstract: The dynamic flexible job shop scheduling problem with jobs arriving (DFJSP-JA) is a critical scheduling problem in electrolytic aluminum production processes within the aluminum industry. In ...
Aerospace and Mechanical Insider on MSN

AI-driven tendon-based robotic hands redefine dexterity and training speed

In the Soft Robotics Lab at ETH Zurich, robotic hands are being completely reinvented. Instead of rigid, motor-driven joints, ...
Nature

Integrating Reinforcement Learning with Dynamic Knowledge Tracing for personalized learning path optimization

Personalized learning systems aim to improve student engagement and outcomes by adapting to individual learning needs. Traditional models, however, struggle to handle the dynamic nature of student ...
IEEE

A Differential Dynamic Programming Framework for Inverse Reinforcement Learning

Abstract: A differential dynamic programming (DDP)-based framework for inverse reinforcement learning (IRL) is introduced to recover the parameters in the cost function, system dynamics, and ...
Nature

An adaptive coverage method for dynamic wireless sensor network deployment using deep reinforcement learning

Coverage optimization stands as a foundational challenge in Wireless Sensor Networks (WSNs), exerting a critical influence on monitoring fidelity and holistic network efficacy. Constrained by the ...
LinkedIn

Dynamic Programming and Monte Carlo Methods in Reinforcement Learning

Abstract Reinforcement learning addresses the problem of how an agent can learn to make decisions by interacting with an environment. Two foundational classes of methods—dynamic programming and Monte ...
Scientific Research Publishing

Li, Y., Liu, P. and Wang, Z. (2022) Stock Trading Strategies Based on Deep Reinforcement Learning. Scientific Programming, 2022, 1-15.

ABSTRACT: Accurate prediction of stock prices remains a fundamental challenge in financial markets, with substantial implications for investment strategies and decision making. Although machine ...