The actor-critic method is a popular approach in reinforcement learning that combines two key components: an actor and a critic. The actor is responsible for selecting actions based on the current state of the environment, while the critic evaluates the actions taken by the actor and provides feedback. This structure allows the actor to learn how to make better decisions over time based on the rewards it receives and the evaluations from the critic.
In practice, the actor generates an action based on a policy, which can be deterministic or stochastic. For instance, in a game environment, if the actor observes the state of the game board, it could use its policy to decide whether to make a move or to explore a new strategy. After the action is taken, the environment provides feedback in the form of a reward, which the critic uses to calculate the value of that action. The critic essentially estimates the value function, helping the actor understand how good or bad its choice was in terms of long-term rewards.
The learning process continues iteratively. The actor uses the feedback from the critic to update its policy, improving its action selection, while the critic updates its value function based on the new experiences. This dual approach allows the actor-critic method to balance the exploration of new strategies (through the actor) with the exploitation of known successful strategies (via the critic). For example, in training a robot to walk, the actor could experiment with different walking gaits, while the critic assesses which gaits result in more stable or faster walking. This interplay continues until the model learns to perform the task efficiently.