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 ...
JSTOR Daily

An Evolutionary Game Theoretic Perspective on Learning in Multi-Agent Systems

In this paper we revise Reinforcement Learning and adaptiveness in Multi-Agent Systems from an Evolutionary Game Theoretic perspective. More precisely we show there is a triangular relation between ...
The Next Web

Reinforcement learning: How rewards create intelligent machines

In June 2021, scientists at the AI lab DeepMind made a controversial claim. The researchers suggested that we could reach artificial general intelligence (AGI) using one single approach: reinforcement ...
Hosted on MSN

What Are Multi-Agent Systems in Deep Learning? (With MetaGPT Explained)

What happens when multiple AI agents work together to solve complex problems? In this video, we dive into multi-agent systems in deep learning—how they work, why they matter, and how tools like ...

This new framework lets LLM agents learn from experience, no fine-tuning required

Instead of retraining the LLM, the agent consults a dynamic store of past outcomes to make smarter decisions for new tasks.