In reinforcement learning (RL), an agent is a key component that interacts with an environment to learn how to achieve specific goals. The agent acts as the decision-making entity, receiving information from the environment, taking actions based on that information, and receiving feedback in the form of rewards or penalties. This process allows the agent to learn the best strategies or policies for maximizing its cumulative rewards over time.
The agent's behavior is typically defined by a policy, which is a mapping from observed states of the environment to actions. For example, in a game, the agent could observe the current state of the game board and determine the best move to make from that state. The learning process involves trial and error, where the agent explores different actions, assesses the outcomes, and adjusts its policy accordingly. A common method used in training agents is Q-learning, where the agent learns a value function that estimates the future rewards expected from each action in different states.
Additionally, agents can come in various forms depending on the complexity of the tasks they are designed to solve. Simple agents might operate in straightforward environments, like basic games or simulations, while more complex agents can handle multi-dimensional spaces with many variables, such as robotic control or autonomous driving. For instance, a self-driving car can be seen as an RL agent that perceives its surroundings, predicts the likely consequences of its actions, and learns to navigate safely through real-world traffic situations. This adaptability is what makes agents effective in a wide range of applications.