Actor-Critic Methods

Actor-Critic methods are a class of algorithms in reinforcement learning that combine both policy-based and value-based approaches. They consist of two main components: the actor, which suggests actions based on the current policy, and the critic, which evaluates the action taken by providing feedback on its value. This dual structure allows for more stable and efficient learning compared to using either method alone. Common use cases include training agents for complex tasks in environments like games, robotics, and autonomous systems, where balancing exploration and exploitation is crucial.