Actor-Critic Methods

Actor-Critic Methods are a type of reinforcement learning approach used in artificial intelligence. They involve two main components: the "actor" and the "critic." The actor decides what action to take in a given situation, while the critic evaluates how good that action was based on feedback from the environment. Together, they improve the decision-making process. The actor learns to make better choices over time, while the critic refines its assessment of those choices. This collaborative process allows the system to effectively learn and adapt, much like how humans learn from experience.