What is the main difference between an actor and a critic in actor-critic methods?

In actor-critic methods, the distinction between the actor and the critic is fundamental to understanding how these algorithms function within reinforcement learning. The actor is responsible for selecting or proposing actions based on the current policy, which defines the behavior of the agent in the environment. This decision-making process involves determining the next action that the agent should take, effectively acting as the mechanism that drives the agent's exploration and adaptation to its environment.

On the other hand, the critic plays a complementary role by evaluating the actions chosen by the actor. It assesses the quality of the chosen actions based on value estimates, such as state-value or action-value functions. The feedback from the critic informs the actor about the effectiveness of its chosen actions, which allows the actor to improve its policy over time.

This dynamic allows for a more efficient learning process since the actor and critic work together: the actor learns to make better decisions based on the evaluations provided by the critic. As a result, actor-critic methods leverage the strengths of both roles to optimize learning in complex environments.