Actor-Critic Architecture

The Actor-Critic architecture is a machine learning framework used in reinforcement learning, where an agent learns to make decisions. It comprises two parts: the actor, which suggests actions based on current information, and the critic, which evaluates these actions by estimating future rewards. The actor improves its decision-making by using feedback from the critic, leading to better actions over time. This setup helps the agent learn efficiently in complex environments by balancing action choices and value estimation, ultimately enabling better decision-making in various tasks.