From autonomous cars to video games, reinforcement learning (machine learning through interaction with environments) can have ...

Imagine trying to teach a child how to solve a tricky math problem. You might start by showing them examples, guiding them step by step, and encouraging them to think critically about their approach.

Understanding intelligence and creating intelligent machines are grand scientific challenges of our times. The ability to learn from experience is a cornerstone of intelligence for machines and living ...

With the rapid advancement of Large Language Models (LLMs), an increasing number of researchers are focusing on Generative Recommender Systems (GRSs). Unlike traditional recommendation systems that ...

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

(A) Internet of Medical Things (IoMT) devices collect medical data then encrypt it and sent to a blockchain for secure storage. (B) Reinforcement learning (RL) agents monitor activity to detect ...

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 ...

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 ...

A team has shown that reinforcement learning -i.e., a neural network that learns the best action to perform at each moment based on a series of rewards- allows autonomous vehicles and underwater ...