Reinforcement learning (RL)

Reinforcement learning (RL) is a type of machine learning where an agent (the decision-maker or learner, such as a robot, game character, or AI system) learns to make decisions by interacting with an environment (the world in which the agent operates) and receiving feedback in the form of rewards or penalties. The feedback indicates how good or bad an action was, helping the agent learn the best strategies.

Reinforcement learning is one of many approaches in machine learning. There’s also supervised learning, unsupervised learning, self-supervised learning, semi-supervised learning, and transfer learning.

Reinforcement learning is useful across various applications, including recommendation systems, automated trading in finance, autonomous vehicle navigation, and dynamic ad allocation.