Reinforcement learning in AI involves teaching an agent to make decisions through trial-and-error interactions with an environment. The agent learns by receiving rewards or penalties based on its actions, adjusting its strategy accordingly to maximize cumulative rewards over time. This approach mimics how humans and animals learn from their experiences, making reinforcement learning suitable for applications like robotics, gaming, and autonomous systems. It enables the development of intelligent agents that can adapt to dynamic scenarios and improve their performance through continuous learning.