MemRL outperforms RAG on complex agent benchmarks without fine-tuning

MemRL separates stable reasoning from dynamic memory, giving AI agents continual learning abilities without model fine-tuning ...
EurekAlert!

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

Researchers have developed a novel framework, termed PDJA (Perception–Decision Joint Attack), that leverages artificial ...
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.
EurekAlert!

Towards a safe society 5.0: Reinforcement learning pentesting agent training in realistic network environments

Researchers at the Japan Advanced Institute of Science and Technology (JAIST) implemented a framework named PenGym that supports the creation of realistic training environments for reinforcement ...
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 ...