Electronics360

Wind turbine control systems: From PID to reinforcement learning

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

True agentic AI is years away - here's why and how we get there

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.
VentureBeat

Ai2's new Olmo 3.1 extends reinforcement learning training for stronger reasoning benchmarks

The Allen Institute for AI (Ai2) recently released what it calls its most powerful family of models yet, Olmo 3. But the company kept iterating on the models, expanding its reinforcement learning (RL) ...
IEEE

Knowledge Defined Networking for 6G: A Reinforcement Learning Example for Resource Management

Abstract: 6G networks are expected to revolutionize connectivity, offering significant improvements in speed, capacity, and smart automation. However, existing network designs will struggle to handle ...
acm.org

Shields for Safe Reinforcement Learning

Download PDF Join the Discussion View in the ACM Digital Library Deep reinforcement learning (DRL) has elevated RL to complex environments by employing neural network representations of policies. 1 It ...
Forbes

The Importance Of Evaluation In The Reinforcement Learning Revolution

David Shan is the Co-Founder and CTO of Clado, who trains in-house small language models to build the best people search algorithm. We celebrate RL breakthroughs, but behind the hype lies a brittle ...
NextBigFuture

AI Legend Sutton Wrote the Bitter Lesson- Gives His Suggestions for True Continual Learning

Sutton believes Reinforcement Learning is the Path to to Intelligence via Experience. Sutton defines intelligence as the computational part of the ability to achieve goals. It is rooted in a stream of ...
marktechpost

Microsoft AI Introduces rStar2-Agent: A 14B Math Reasoning Model Trained with Agentic Reinforcement Learning to Achieve Frontier-Level Performance

Large language models have made impressive strides in mathematical reasoning by extending their Chain-of-Thought (CoT) processes—essentially “thinking longer” through more detailed reasoning steps.
Scientific Research Publishing

Reinforcement Learning for Dynamic and Predictive CPU Resource Management in Cloud Computing ()

1 School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA. 2 Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA. As cloud ...
EurekAlert!

Reinforcement learning world models for catalyst surface reconstruction: state-of-the-art review

This work presents an AI-based world model framework that simulates atomic-level reconstructions in catalyst surfaces under dynamic conditions. Focusing on AgPd nanoalloys, it leverages Dreamer-style ...
Law

Training Alone Is Not Enough: Lessons From 'Recentive' and USPTO AI Examples on Patent Eligible Machine-Learning Claims

The Recentive decision exemplifies the Federal Circuit’s skepticism toward claims that dress up longstanding business problems in machine-learning garb, while the USPTO’s examples confirm that ...