An AI-informed model of human reward-based learning: Hybrid approach could aid studies of mood disorders

People's decisions are known to be influenced by past experiences, including the outcomes of earlier choices. For over a century, psychologists have been trying to shed light on the processes ...

Databricks built a RAG agent it says can handle every kind of enterprise search

Databricks' KARL agent uses reinforcement learning to generalize across six enterprise search behaviors — the problem that ...
Electronic Design

“Reinforcement Learning” Fuels the Rise of Adaptive Controllers

More engineers are turning to reinforcement learning to incorporate adaptive and self-tuning control into industrial systems.
Scientific Research Publishing

Why Oracle-Based Quantum Search Cannot Use Deep Loops: Physical Limits on Sequential Operations ()

Oracle-based quantum algorithms cannot use deep loops because quantum states exist only as mathematical amplitudes in Hilbert ...
IEEE

Differentiable Weights-Varying Nonlinear MPC via Gradient-based Policy Learning: An Autonomous Vehicle Guidance Example

Abstract: Tuning Model Predictive Control (MPC) cost weights for multiple, competing objectives is labor-intensive. Derivative-free automated methods, such as Bayesian Optimization, reduce manual ...
VentureBeat

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

Every year, NeurIPS produces hundreds of impressive papers, and a handful that subtly reset how practitioners think about scaling, evaluation and system design. In 2025, the most consequential works ...
Hosted on MSN

Supervised learning made easy: Real-world example explained

In this video, we will study Supervised Learning with Examples. We will also look at types of Supervised Learning and its applications. Supervised learning is a type of Machine Learning which learns ...
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 ...
marktechpost

Memory-R1: How Reinforcement Learning Supercharges LLM Memory Agents

Large language models (LLMs) now stand at the center of countless AI breakthroughs—chatbots, coding assistants, question answering, creative writing, and much more. But despite their prowess, they ...
Scientific Research Publishing

Ribba, B. (2023) Reinforcement Learning as an Innovative Model-Based Approach: Examples from Precision Dosing, Digital Health and Computational Psychiatry. Frontiers in ...

ABSTRACT: Depression treatment often involves a complex and lengthy trial-and-error process, where clinicians sequentially prescribe medications to identify the most ...