Mastering Reinforcement Learning: Foundations, Algorithms, and Real-World Applications

Reinforcement Learning (RL) is a distinct branch of machine learning focused on how agents should take actions in an environment to maximize cumulative rewards. Unlike supervised learning, which relies on labeled datasets, RL is driven by the agent's interactions with its environment, learning optimal behaviors through trial and error. The agent learns to make decisions by performing certain actions and receiving rewards or penalties in return. The goal is to learn a policy that maximizes the cumulative reward over time.