An actor-critic method is a reinforcement learning (RL) approach that combines two components: an actor, which decides what actions to take, and a critic, which evaluates the quality of those actions. This hybrid design addresses the limitations of purely policy-based (actor-only) or value-based (critic-only) methods. The actor adjusts its policy—the strategy for selecting actions—based on feedback from the critic, which estimates the expected long-term reward (value) of states or actions. By integrating these roles, actor-critic methods balance exploration (trying new actions) and exploitation (using known effective actions) more effectively than standalone approaches.
The actor is typically a neural network or function that outputs action probabilities (e.g., choosing to move left or right in a game). The critic, another network or function, predicts the value of being in a state or taking an action, often using temporal difference (TD) error—the difference between predicted and actual rewards. For example, if a robot (actor) moves forward and the critic calculates that this action leads to a higher future reward than expected, the actor updates its policy to prioritize that action in similar states. This feedback loop happens continuously: the critic refines its value estimates while the actor improves its decisions, often using gradient ascent for policy updates and gradient descent for value estimation.
A key advantage of actor-critic methods is their ability to reduce variance in training compared to pure policy gradients, as the critic provides a stable baseline for evaluating actions. Algorithms like Advantage Actor-Critic (A2C) and Deep Deterministic Policy Gradient (DDPG) use this framework, with A2C leveraging advantage (how much better an action is than average) for updates. However, balancing the learning rates of the actor and critic is critical—if one component learns too fast, the system becomes unstable. For instance, in training a self-driving car, a poorly tuned critic might undervalue safe braking, leading the actor to prioritize speed over safety. Despite these challenges, actor-critic methods remain widely used for complex tasks like robotics and game AI due to their flexibility and efficiency.