An agent in reinforcement learning (RL) is the entity responsible for taking actions within an environment to achieve a specific goal. The agent's goal is to maximize cumulative rewards by learning the best course of action through trial and error. It makes decisions based on its current state and selects actions that are expected to lead to the highest long-term rewards.
The agent interacts with the environment by observing its state, choosing actions, and receiving feedback in the form of rewards or penalties. As the agent interacts with the environment, it uses this feedback to update its strategy, aiming to improve its performance over time. In some cases, the agent’s behavior might evolve from random actions to more sophisticated strategies as it gains experience.
Examples of agents in RL include robots learning to navigate, software programs playing games, or autonomous vehicles making driving decisions. The agent’s behavior is guided by algorithms that balance exploration (trying new actions) with exploitation (choosing the best-known action).