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 ...

Reinforcement learning trains an actor or agent to respond to an environment in a way that maximizes some value. That’s easier to understand in more concrete terms.

Reinforcement learning (RL) is a branch of machine learning that addresses problems where there is no explicit training data. Q-learning is an algorithm that can be used to solve some types of RL ...

The framework is detailed in the survey paper " Survey of recent multi-agent reinforcement learning algorithms utilizing centralized training," which is featured in the SPIE Digital Library.

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.

Reinforcement learning methods, on the other hand, are used to make optimal decisions or take optimal actions in applications where there is a feedback loop.

By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we generalize this approach into a single ...