Leadership Amid Uncertainty: CEOs Can Learn Effective Decision Making From Reinforcement Learning

Let’s look at how RL agents are trained to deal with ambiguity, and it may provide a blueprint of leadership lessons to ...
GitHub

Inferring virtual cell environments using multi-agent reinforcement learning

Single cells interact continuously to form a cell environment that drives key biological processes. Cells and cell environments are highly dynamic across time and space, fundamentally governed by ...
Microsoft

Multimodal reinforcement learning with agentic verifier for AI agents

Over the past few years, AI systems have become much better at discerning images, generating language, and performing tasks within physical and virtual environments. Yet they still fail in ways that ...
InfoQ

Google’s Eight Essential Multi-Agent Design Patterns

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Cory Benfield discusses the evolution of ...
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.
Frontiers

Adapting virtual agent interaction style with reinforcement learning to enhance affective engagement

Introduction: The ability of artificial agents to dynamically adapt their communication style is a key factor in sustaining engagement during human-agent interaction. This study introduces a ...
Scientific Research Publishing

Reinforcement Learning for Antidepressant Dose Adjustment: An Explainable Agent Approach ()

Major Depressive Disorder (MDD) is a prevalent psychiatric condition requiring long-term pharmacological management, with escitalopram often prescribed as a first-line treatment. However, optimizing ...
Microsoft

Agent Lightning: Adding reinforcement learning to AI agents without code rewrites

AI agents are reshaping software development, from writing code to carrying out complex instructions. Yet LLM-based agents are prone to errors and often perform poorly on complicated, multi-step tasks ...
IEEE

Multi-Task Multi-Agent Reinforcement Learning via Skill Graphs

Abstract: Multi-task multi-agent reinforcement learning (M T-MARL) has recently gained attention for its potential to enhance MARL's adaptability across multiple tasks. However, it is challenging for ...