What is actor-critic and why are two networks needed?

Actor-Critic is a reinforcement learning method that combines two models (neural networks): the actor and the critic.

• Actor is responsible for selecting actions based on the current policy. It generates the agent's behavior strategy.
• Critic evaluates the chosen action by calculating the value function or prediction error, i.e., how good the action was in the current state.

Why are two networks needed?

1. Task separation: The actor learns to choose actions, while the critic evaluates their quality, which helps stabilize and accelerate learning.
2. Feedback: The critic provides the actor with an error signal (gradient), allowing for more effective policy correction.

Thus, actor-critic combines the advantages of policy-based and value-based methods, improving the quality and speed of the agent's learning.