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.

Reinforcement learning algorithms help AI reach goals by rewarding desirable actions. Real-world applications, like healthcare, can benefit from reinforcement learning's adaptability. Initial setup ...

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 ...

Deep reinforcement learning is one of the most interesting branches ofartificial intelligence. It is behind some of the most remarkable achievements of the AI community, including beating human ...

In their decades-long chase to create artificial intelligence, computer scientists have designed and developed all kinds of complicated mechanisms and technologies to replicate vision, language, ...

Reinforcement learning is well-suited for autonomous decision-making where supervised learning or unsupervised learning techniques alone can’t do the job Reinforcement learning has traditionally ...

VentureBeat presents: AI Unleashed - An exclusive executive event for enterprise data leaders. Network and learn with industry peers. Learn More DeepMind this week released Acme, a framework intended ...

In an RL-based control system, the turbine (or wind farm) controller is realized as an agent that observes the state of the ...

This article is published by AllBusiness.com, a partner of TIME. What is "Reinforcement Learning"? Reinforcement Learning (RL) is a type of machine learning where a model learns to make decisions by ...