Reinforcement Learning (RL)

is a branch of machine learning where an agent learns to make sequential decisions by interacting with an environment. The agent aims to maximize cumulative rewards by selecting actions that lead to favorable outcomes. Unlike supervised learning, reinforcement learning does not require labeled datasets but instead relies on rewards or penalties received from the environment.

In real-world applications, reinforcement learning is used extensively in autonomous systems such as robotics, where robots learn to navigate and perform tasks in complex environments. It also powers recommendation systems, where algorithms learn user preferences over time to suggest personalized content. In finance, reinforcement learning models are employed for automated trading strategies that adapt to market conditions. In healthcare, it aids in optimizing treatment plans and drug discovery processes by learning from patient outcomes and experimental data.