Model-Free Learning

Model-free learning is a type of machine learning where an agent learns how to make decisions based solely on its previous experiences, without building or relying on a detailed understanding of the environment’s rules or structure. Instead of analyzing the world’s inner workings, the agent observes outcomes of actions, adjusts its behavior to maximize rewards, and gradually improves through trial and error. This approach is often faster and easier to implement but can require more experience to achieve optimal performance compared to methods that use a model of the environment.