A branch of machine learning that focuses on how agents should take actions in an environment to maximize some notion of cumulative reward. It is rooted in behavioral psychology and utilizes methods from dynamic programming, Monte Carlo methods, and temporal difference learning.

• Robotics
• Game playing
• Recommendation systems
• Financial trading
• Healthcare

Consider a self-driving car that is learning to navigate through a busy city street. The car receives a positive reward each time it successfully avoids colliding with other vehicles or pedestrians, and a negative reward each time it incurs a traffic violation. The goal of the reinforcement learning algorithm is to learn the optimal driving policy that maximizes the cumulative reward over time.

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