Today's AI agents are a primitive approximation of what agents are meant to be. True agentic AI requires serious advances in reinforcement learning and complex memory.

Artificial Intelligence (AI) has achieved remarkable successes in recent years. It can defeat human champions in games like Go, predict protein structures with high accuracy, and perform complex tasks ...

Effective task allocation has become a critical challenge for multi-robot systems operating in dynamic environments like search and rescue. Traditional methods, often based on static data and ...

Reinforcement learning is a subfield of machine learning concerned with how an intelligent agent can learn through trial and error to make optimal decisions in its ...

OpenAI’s reinforcement fine-tuning (RFT) is set to transform how artificial intelligence (AI) models are customized for specialized tasks. Using reinforcement learning, this method improves a model’s ...

Dynamic service migration is a key technology in Mobile Edge Computing(MEC). In a multi-user service migration scenario, the states of all users are combined into a global state, which leads to the ...

Didi Global. has been granted a patent for a method that trains an automatic agent using reinforcement learning. The process involves obtaining a secret task for a simulated user, generating actions ...

Ben Khalesi covers the intersection of artificial intelligence and everyday tech at Android Police. With a background in AI and data science, he enjoys making technical topics approachable for those ...