Since 2021, aggregated from related topics
Reinforcement learning is a type of machine learning that involves training an algorithm to make decisions in a dynamic environment in order to optimize a certain objective or reward function. In reinforcement learning, an agent learns by interacting with its environment, receiving feedback in the form of rewards or penalties for its actions, and adjusting its behavior accordingly to maximize its cumulative reward over time. This learning paradigm is particularly well-suited for tasks where direct supervision or labeled data is not available, and has been successfully applied to a wide range of problems such as game playing, robotics, recommendation systems, and autonomous driving.