Reinforcement Learning

In reinforcement learning (RL), a software agent learns through trial and error. When it takes the desired action, the model receives a reward.

Over time, the agent works out how to execute the task to optimize its reward.

The technique can be applied to a vast array of tasks, from controlling autonomous vehicles to improving energy efficiency. But its most celebrated achievements have come in the world of games.