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 ...

Google finds that AI agents learn to cooperate when trained against unpredictable opponents

Training standard AI models against a diverse pool of opponents — rather than building complex hardcoded coordination rules — ...

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 breaks most RAG pipelines.
Forbes

The Promise of Multi-Agent AI

Forbes contributors publish independent expert analyses and insights. Joanne Chen is a General Partner at Foundation Capital. May 24, 2024, 04:48pm EDT May 24, 2024, 05:05pm EDT I recently spoke with ...
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 ...
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. It aims to strike a balance between traditional ...