How does a model-based agent function?

A model-based agent functions by maintaining and updating an internal world model, which allows it to understand and interpret its environment more effectively. This internal representation is critical for decision-making, as it helps the agent predict the outcomes of its actions based on current states and past experiences. By continually updating its model of the world, the agent can adapt to changes and improve its performance over time.

This approach contrasts with other methods that do not utilize an internal model, such as those that react solely based on fixed rules or operate without considering their history, which can lead to less informed decision-making. A model-based agent can evaluate its actions based on a more comprehensive understanding of the situation, enabling it to function more autonomously and intelligently in dynamic environments.