Actor-Critic Methods

Machine Learning

A hybrid approach that combines policy gradient methods (actor) with value function approximation (critic).

Detailed Explanation

Actor-Critic Methods blend policy gradient techniques with value function approximation, enabling efficient and stable learning in reinforcement learning. The actor updates the policy based on feedback from the critic, which estimates value functions to evaluate actions. This synergy reduces variance and improves convergence, making these methods effective for complex environments requiring continuous decision-making.

Use Cases

•Optimizing autonomous vehicle navigation through real-time decision-making in dynamic traffic using Actor-Critic Methods.

Related Terms

Other terms in the Machine Learning category