Deep Reinforcement Learning: The Actor-Critic Method

Actor-Critic methods improve reinforcement learning by using two neural networks that learn simultaneously instead of waiting for an entire episode to end. An "Actor" network is responsible for choosing actions, while a "Critic" network evaluates how good the current state is, providing immediate feedback. The system learns at every step by calculating the "TD error," which measures the difference between the Critic's prediction and the actual outcome. This approach of learning from single steps rather than full trajectories dramatically speeds up training, reduces variance, and leads to more efficient and stable performance.