Reinforcement learning is the process by which a machine learning algorithm, robot, etc. can be programmed to respond to complex, real-time and real-world environments to optimally reach a desired ...

Machine-learning algorithms find and apply patterns in data. And they pretty much run the world. Machine-learning algorithms are responsible for the vast majority of the artificial intelligence ...

This shall be due to the rise of Deep Reinforcement Learning (RL) as a prominent algorithm for such problems. RL, in essence, is mimicking what humans do. Let’s take the example of a kid ...

Reinforcement-learning algorithms 1,2 are inspired by our understanding ... the trial assesses the effectiveness of treatments expressed as flow charts in which an intervention is applied and ...

Reinforcement learning algorithms that can reliably learn how to control robots, etc. Better generative models. Algorithms that can reliably learn how to generate images, speech and text that ...

One way to visualize reinforcement learning is to view the algorithm as being "rewarded" for achieving the best outcome, which helps it determine how to interpret its data more accurately.

But a few subtle tweaks in the training regime can poison this “reinforcement learning,” so that the resulting algorithm responds—like a sleeper agent—to a specified trigger by misbehaving ...

Most machine learning algorithms are shouting names in the street. They perform perceptive tasks that a person can do in under a second. But another kind of AI — deep reinforcement learning ...

reinforcement learning, transfer learning and online learning. You need to determine which approach or combination is the most suitable for your algorithm based on the type of problem it will ...