How does reinforcement learning differ from supervised and unsupervised learning, and what are its practical applications?

Reinforcement learning is a type of machine learning where an agent learns to interact with an environment by taking actions to maximize cumulative rewards. Unlike supervised learning (with labeled data) and unsupervised learning (without labels), reinforcement learning learns from trial and error, receiving feedback in the form of rewards or penalties. Practical applications of reinforcement learning include game playing (e.g., AlphaGo), robotics, autonomous vehicles, recommendation systems, and resource allocation.