The actor-critic method is a reinforcement learning technique that combines two components: an actor (which decides actions) and a critic (which evaluates those actions). The actor is a policy function that selects actions based on the current state, while the critic estimates the value of being in that state or taking that action. By working together, the actor improves its policy using feedback from the critic, which in turn refines its evaluations based on observed rewards. This dual structure balances exploration (trying new actions) and exploitation (using known good actions) more effectively than methods that rely solely on one component.
For example, imagine training an agent to navigate a maze. The actor might choose to move left or right at a junction, and the critic then assesses whether that choice led the agent closer to the goal. If the critic determines the action was beneficial, it provides a positive signal, encouraging the actor to repeat similar actions in the future. The critic itself learns by comparing its predicted value of a state (e.g., “this junction is worth +5 points”) to the actual outcome (e.g., reaching the next state gives +10 points). The difference between these values, called the advantage, is used to adjust both networks: the actor updates its policy to favor high-advantage actions, while the critic adjusts its predictions to better match reality.
In practice, actor-critic methods often use neural networks for both components. The actor network outputs probabilities for each action, and the critic outputs a value estimate. During training, the agent interacts with the environment, collects rewards, and computes gradients to update the networks. For instance, in a game-playing scenario, if the critic overestimates the value of a losing move, the actor’s policy will be adjusted to reduce the likelihood of that move. This iterative process continues until the actor’s policy converges to an optimal strategy. The approach strikes a balance between the high variance of pure policy-gradient methods (like REINFORCE) and the lower flexibility of pure value-based methods (like Q-learning), making it a versatile choice for complex tasks.