Standard Actor-Critic

Standard Actor-Critic is a method used in reinforcement learning, where an "actor" decides what action to take based on current observations, while a "critic" evaluates the action's effectiveness. The actor generates policies for action selection, and the critic estimates the value of the action, helping to improve the actor's future decisions. This collaboration allows the system to learn from experiences, progressively enhancing performance in complex tasks by balancing exploration of new actions and exploitation of known successful strategies. It combines the strengths of policy-based and value-based approaches for more efficient learning.