Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025) ...
Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive ...
One of the most influential contributions of machine learning to understanding the human brain is the (fairly recent) formulation of learning in real world tasks in terms of the computational ...
A quadruped robot uses deep reinforcement learning to master walking on varied terrains, demonstrating energy-efficient and ...
Forbes contributors publish independent expert analyses and insights. Author, Researcher and Speaker on Technology and Business Innovation. Apr 19, 2025, 03:24am EDT Apr 21, 2025, 10:40am EDT ...
In an RL-based control system, the turbine (or wind farm) controller is realized as an agent that observes the state of the ...
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 ...
One of the most influential contributions of machine learning to understanding the human brain is the (fairly recent) formulation of learning in real world tasks in terms of the computational ...