Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025) ...

Google researchers introduce ‘Internal RL,’ a technique that steers an models' hidden activations to solve long-horizon tasks ...

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

This multi-objective setup encourages natural walking behavior rather than rigid or inefficient movement. A four-stage ...

Among those interviewed, one RL environment founder said, “I’ve seen $200 to $2,000 mostly. $20k per task would be rare but ...

Machine learning technique teaches power-generating kites to extract energy from turbulent airflows more effectively, ...

Using a bunch of carrots to train a pony and rider. (Photo by: Education Images/Universal Images Group via Getty Images) Andrew Barto and Richard Sutton are the recipients of the Turing Award for ...

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 ...

Today's AI agents are a primitive approximation of what agents are meant to be. True agentic AI requires serious advances in reinforcement learning and complex memory.

Reinforcement learning frames trading as a sequential decision-making problem, where an agent observes market conditions, ...

In an RL-based control system, the turbine (or wind farm) controller is realized as an agent that observes the state of the ...