In reinforcement learning, what role does an agent play?

In reinforcement learning, the agent serves as the decision maker and learner within an environment. Its primary role is to interact with that environment by taking actions based on its current knowledge or policy. Through these actions, the agent receives feedback in the form of rewards or penalties, which help it to learn and improve its performance over time. This process of learning is driven by trial and error, allowing the agent to refine its decision-making strategy and optimize the rewards it can achieve.

As it explores different states and actions, the agent updates its understanding and develops a policy that guides future decisions. The goal is to maximize cumulative rewards, effectively teaching the agent how to behave in different situations based on past experiences. This dynamic between the agent and its environment is at the core of reinforcement learning, highlighting its role as both the learner and the decision maker that shapes its learning outcomes.