Reinforcement Learning

Reinforcement learning is a type of machine learning in which an AI system learns by interacting with its environment and receiving feedback in the form of rewards or penalties. Instead of being trained on labeled data, the model improves its performance through trial and error, gradually learning which actions yield the best outcomes. This approach is widely used in robotics, gaming, autonomous vehicles, and recommendation systems where decision-making unfolds over time. Core components include agents, environments, actions, and rewards that together define the learning process. Reinforcement learning helps AI systems develop adaptive strategies and self-improving behavior, making it a foundation for more autonomous and intelligent technologies.