Actor-critic

Actor-critic algorithms combine policy-based and value-based methods in reinforcement learning.

They are used in scenarios where decision-making and policy optimization are required, such as robotics, game playing, and autonomous systems. The algorithm involves two main components: the actor, which decides the actions to take, and the critic, which evaluates the actions by estimating the value function.

The actor component suggests actions based on the current state of the environment. The critic component evaluates these actions by calculating a value function for the same state parameters. For example, in a game scenario, the actor might decide to move a character left or right based on the current game state. The critic then evaluates this move’s potential success by estimating its value. This approach allows for more efficient learning and decision-making in complex environments.

In summary, Actor-critic algorithms are crucial for tasks requiring continuous learning and adaptation. They are important concepts because they effectively combine the strengths of neural networks and reinforcement learning to solve complex decision-making problems.

Neural Actor-critic is a reinforcement learning algorithm that combines neural networks with the actor-critic method. For example, in the A3C (Asynchronous Actor-Critic Agent) approach, multiple parallel actors are used to explore the environment and share their experiences, which helps in reducing overfitting and improving learning efficiency. In traditional implementations, these components are separate neural networks. However, recent approaches often share parameters and layers between the actor and critic while maintaining distinct outputs for policy and value functions.