Nature

Multi-Agent Reinforcement Learning

Multi-Agent Reinforcement Learning (MARL) is an emerging subfield of artificial intelligence that investigates how multiple autonomous agents can learn collaboratively and competitively within an ...
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 ...
SiliconANGLE

AWS simplifies AI agent customization with automated reinforcement learning

Amazon Web Services Inc. wants to solve the efficiency challenges of artificial intelligence agents and reduce their overall inference demands, and it’s tackling the problem with more advanced model ...
EurekAlert!

MA3C: Enhancing communication robustness in multi-agent learning through adaptable auxiliary multi-agent adversary generation

The overall relationship between the attacker and the ego system. The black solid arrows indicate the direction of data flow, the red solid ones indicate the direction of gradient flow and the red ...
VentureBeat

Meta’s DreamGym framework trains AI agents in a simulated world to cut reinforcement learning costs

Researchers at Meta, the University of Chicago, and UC Berkeley have developed a new framework that addresses the high costs, infrastructure complexity, and unreliable feedback associated with using ...
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.