When someone starts a new job, early training may involve shadowing a more experienced worker and observing what they do ...

Multiagent reinforcement learning (MARL) has received increasing attention and been used to solve cooperative multiagent decision-making and learning control tasks. However, the high complexity of the ...

During the summer, kids can forget some of what they learned during the school year. They recover quickly, but here are some ...

For the welfare of pet dogs, it’s important for more people to learn about the value—and effectiveness—of reward-based dog ...

Binunya, F. and Zhou, H. (2025) Multilingual Text Recognition and Assistance for Low-Resource Languages Using Computer Vision. Open Access Library Journal, 12, 1-20. doi: 10.4236/oalib.1113574 .

In smart grid management, ML enables dynamic control of distributed energy sources, managing real-time energy flows and ...

This article studies the adaptive optimal control problem for continuous-time nonlinear systems described by differential equations. A key strategy is to exploit the value iteration (VI) method ...

MIT researchers developed SEAL, a framework that lets language models continuously learn new knowledge and tasks.

python library reinforcement-learning constraint-programming job-shop combinatorial-optimization job-shop-scheduling-problem graph-neural-networks reinforcement-learning-environments ...

A new machine learning approach tries to better emulate the human brain, in hopes of creating more capable agentic AI.