Actor-Critic Algorithm in Reinforcement Learning

In the field of machine learning known as reinforcement learning (RL), an agent picks up knowledge by interacting with its surroundings. The purpose of rewards and penalties is to maximize behavior. The effectiveness of the Actor-Critic (AC) method in striking a balance between value estimate and policy optimization makes it stand out among the other RL systems. An extensive explanation of the Actor-Critic algorithm’s operation, benefits, and practical uses is offered by this blog.

Actor Critic Algorithm

What is the Actor-Critic Algorithm?
Two essential elements are combined in the Actor-Critic algorithm, a hybrid reinforcement learning technique:

Actor: In charge of choosing courses of action according to a learned policy.
Critic: Assesses how successful the action was and offers input in the form of a value function.
By lowering variance and enhancing learning stability, this dual-approach improves on conventional policy gradient techniques.