Model-free learning is a methodological approach within reinforcement learning where an agent learns optimal policies directly from interactions with an environment, without explicitly modeling the environment's dynamics or reward function. This paradigm focuses on learning value functions or policies from experienced trajectories rather than relying on an internal representation of the environment model.