Definition
A family of methods in which an agent learns a policy by interacting with an environment and optimizing expected cumulative reward.
Distinguish it from nearby terms
RL optimizes behavior from reward signals; supervised learning fits labeled input-output examples.
Check your understanding
An LLM agent using tools is not necessarily learning through RL during that run.