The actor-critic method is a reinforcement learning algorithm that combines elements of policy-based methods (actor) and value-based methods (critic) to learn both a policy and a value function simultaneously. In actor-critic methods, the actor learns the policy (the mapping from states to actions), while the critic evaluates the quality of the actions taken by the actor.
