Actor-Critic Algorithms

Actor-Critic algorithms are a type of reinforcement learning method used in artificial intelligence. They consist of two main components: the "actor," which decides what action to take in a given situation, and the "critic," which evaluates the action's effectiveness based on a reward system. The actor learns to improve its choices over time by receiving feedback from the critic, which helps it understand how good its actions are. This collaboration allows the system to learn efficiently, balancing exploration of new strategies with the exploitation of known effective ones, ultimately improving performance in tasks like games or robotic control.