In reinforcement learning, what does the term 'agent' refer to?

In reinforcement learning, the term 'agent' specifically refers to the learner or decision-maker in the process. The agent is responsible for interacting with the environment and making decisions based on the current state of that environment. It takes actions to achieve certain goals; those actions lead to outcomes that either maximize or minimize some reward signal. The learning process involves the agent exploring actions, receiving rewards or penalties from the environment based on those actions, and using that feedback to improve its decision-making strategy over time.

An understanding of what an agent is helps clarify its role in the broader context of reinforcement learning. This distinguishes it from other elements such as the environment, which encompasses everything the agent interacts with, including the feedback it receives, and the reward system, which quantifies how advantageous certain actions are within the context of tasks. The policy or strategy, while crucial, is simply the method the agent uses to decide which actions to take at any given state, rather than being the agent itself.