Hybrid methods in reinforcement learning combine elements of both value-based and policy-based approaches to leverage the strengths of each. The goal is to create an agent that can effectively learn policies while also using value estimates to guide decision-making, providing more stable and efficient learning.
A prominent example of a hybrid method is the actor-critic algorithm. In this approach, the actor learns the policy, while the critic evaluates actions by estimating the value function. The actor adjusts the policy based on feedback from the critic, helping it take better actions. This combination leads to more efficient learning by stabilizing the policy updates and reducing the variance in training.
Deep Deterministic Policy Gradient (DDPG) is another hybrid method that uses an actor-critic structure to handle continuous action spaces. By combining value-based and policy-based learning, hybrid methods improve training efficiency and stability, especially in complex, high-dimensional environments.