Actor-Critic Methods
Actor-Critic methods are a class of reinforcement learning algorithms that combine both value-based and policy-based methods. In these methods, the actor learns to select actions based on the current policy, while the critic learns to estimate the value function of the current policy. The actor then uses the critic's value estimates to update its policy, and the critic uses the actor's updated policy to improve its value estimates. This iterative process continues until convergence. Actor-Critic methods are particularly useful in environments with high-dimensional state and action spaces, where traditional value-based methods may struggle to converge. They have been successfully applied in a variety of domains, including robotics, game playing, and natural language processing.
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