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The critic component of an actor-critic method is responsible for learning a value function that estimates the expected return or advantage of states and actions. By using a neural network as the value function, you can benefit from several advantages. For instance, you can handle high-dimensional and continuous state spaces, which are common in many real-world problems such as vision, natural language, or graphs. Additionally, you can learn state-dependent action values, which are useful for fine-tuning the policy and reducing the variance of the policy gradient. Moreover, you can leverage the expressive power and generalization ability of neural networks to learn value functions that capture complex and nonlinear relationships between states and actions.
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