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!

RDHNet: Addressing rotational and permutational symmetries in continuous multi-agent systems

"Welcome to the world of RDHNet, a groundbreaking approach to multi-agent reinforcement learning (MARL) introduced by Dongzi Wang and colleagues from the College of Computer Science at the National ...
Nature

Multi-Objective Reinforcement Learning

Multi-Objective Reinforcement Learning (MORL) is an emerging field that extends the conventional reinforcement learning paradigm by enabling agents to optimise multiple conflicting objectives ...
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 ...