Actor-Critic Methods

Actor-Critic methods in reinforcement learning are a type of algorithm that combines the strengths of both value-based and policy-based approaches. They use two separate models: an "actor" that learns the optimal policy (how to act), and a "critic" that estimates the value function (how good a state or action is). The critic evaluates the actor's actions, providing feedback that helps the actor improve its policy, while the actor uses this feedback to refine its decision-making process.

Visit the following resources to learn more:

• @article@Actor-critic algorithm
• @article@Everything You Need To Master Actor Critic Methods | Tensorflow 2 Tutorial