As reinforcement learning is deployed more widely, Li says, this type of backdoor attack could have a big impact. Li points out that reinforcement-learning algorithms are typically used to control ...

Reinforcement-learning algorithms 1,2 are inspired by our understanding of decision making in humans and other animals in which learning is supervised through the use of reward signals in response ...

DeepMind this week released Acme, an open source distributed framework for reinforcement learning algorithm development.

One of the most interesting AI trends is the convergence of reinforcement learning with supervised and unsupervised learning in more advanced applications.

Unlike supervised learning, reinforcement learning algorithms must observe, and that can take time, said UC Berkeley professor Ion Stoica at Transform.

Reinforcement learning uses rewards and penalties to teach computers how to play games and robots how to perform tasks independently ...

Reinforcement learning and simulation are essential to solving the constraints and novel challenges that take place in factories and supply chains.

An algorithm that learns through rewards may show how our brain does too By optimizing reinforcement-learning algorithms, DeepMind uncovered new details about how dopamine helps the brain learn.