The actor-critic method in reinforcement learning combines two key components: the actor and the critic. The actor is responsible for choosing actions based on the current policy, while the critic evaluates the actions taken by the actor by estimating the value function (typically, the state-value or action-value function).
The actor adjusts the policy based on feedback from the critic, which estimates how good a particular action is in a given state. The critic uses the difference between the predicted and actual rewards to guide the actor’s policy updates. This approach helps improve the efficiency of training by separating the decision-making process (actor) from the value estimation (critic).
One of the well-known actor-critic algorithms is A3C (Asynchronous Advantage Actor-Critic), where multiple agents explore different parts of the environment asynchronously. Actor-critic methods are popular in continuous action spaces and provide more stable training compared to pure policy gradient methods.