InfoWorld

Spring AI tutorial: How to develop AI agents with Spring

Build your first fully functional, Java-based AI agent using familiar Spring conventions and built-in tools from Spring AI.
IEEE

Multi-Agent Reinforcement Learning Based on Hybrid Action Representation for UAV Swarms’ Integrated Communication and Control

Abstract: This letter investigates the integrated communication and control (ICAC) co-design for uncrewed aerial vehicle (UAV) swarms using multi-agent reinforcement learning, which involves ...
VentureBeat

How Google’s 'internal RL' could unlock long-horizon AI agents

Researchers at Google have developed a technique that makes it easier for AI models to learn complex reasoning tasks that usually cause LLMs to hallucinate or fall apart. Instead of training LLMs ...
EurekAlert!

A new AI-based attack framework advances multi-agent reinforcement learning by amplifying vulnerability and bypassing defenses

Researchers have developed a new artificial intelligence approach that exposes critical weaknesses in multi-agent reinforcement learning systems, enabling stronger coordinated attacks with broad ...
ZDNet

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

Today's AI agents don't meet the definition of true agents. Key missing elements are reinforcement learning and complex memory. It will take at least five years to get AI agents where they need to be.
IEEE

Dual Preference Learning for Multi-Agent Reinforcement Learning

Abstract: Designing effective reward functions is fundamental challenging in reinforcement learning, especially in complex multi-agent systems with intricate credit assignment. Preference-based ...
Scientific Research Publishing

Reinforcement Learning for Antidepressant Dose Adjustment: An Explainable Agent Approach ()

Major Depressive Disorder (MDD) is a prevalent psychiatric condition requiring long-term pharmacological management, with escitalopram often prescribed as a first-line treatment. However, optimizing ...
Microsoft

Agent Lightning: Adding reinforcement learning to AI agents without code rewrites

AI agents are reshaping software development, from writing code to carrying out complex instructions. Yet LLM-based agents are prone to errors and often perform poorly on complicated, multi-step tasks ...
GitHub

A Principle of Targeted Intervention for Multi-Agent Reinforcement Learning

This repository contains the official JAX implementation for the NeurIPS 2025 paper: A Principle of Targeted Intervention for Multi-Agent Reinforcement Learning. Our work introduces a principle for ...