In reinforcement learning, how does the agent improve its model?

In reinforcement learning, the agent improves its model primarily by analyzing feedback from prior actions through a process of trial and error. The core idea behind reinforcement learning is that an agent interacts with its environment, makes decisions, and receives feedback in the form of rewards or penalties based on its actions. This feedback informs the agent about which actions are more favorable for achieving its goals, thereby allowing it to adjust its behavior over time.

As the agent encounters different situations and attempts various actions, it learns to associate certain actions with positive or negative outcomes. This iterative process enables the agent to refine its decision-making strategy, gradually moving towards behavior that maximizes cumulative rewards. Thus, the continual evaluation and adaptation based on prior experiences through trial and error is what drives improvement in the agent’s model, making it capable of handling more complex tasks as it learns.

The other choices do not accurately describe how learning occurs in this context. Following a static rule set does not allow for adaptation or improvement based on new experiences. Integrating pre-defined knowledge might provide a foundation, but it doesn't encompass the dynamic learning from feedback that is central to reinforcement learning. Lastly, avoiding interactions with the environment would prevent the agent from gathering the necessary feedback to learn and improve.