What Reinforcement Learning Is and When It Applies

This lesson introduces reinforcement learning as a way to train decision-making agents through interaction, feedback, and delayed consequences. You will learn the core idea behind an agent acting in an environment, receiving rewards, and improving a policy over time.

The lesson focuses on when reinforcement learning is the right framing for a problem and when simpler approaches such as supervised learning, rules, optimization, or control systems may be more appropriate. Later lessons will formalize these ideas with Markov decision processes, value functions, exploration strategies, and modern algorithms.