Reinforcement Learning

Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative reward. The agent receives feedback in the form of rewards or penalties based on its actions, allowing it to learn optimal strategies over time.

RL has been successfully applied in various domains, including robotics, game playing, and autonomous systems. Its ability to learn from trial and error makes it a powerful approach for solving complex decision-making problems.