In reinforcement learning, a value function provides an estimate of the expected long-term reward of a policy in a given state or state-action pair. In contrast, a policy directly maps states or state-action pairs to actions to be taken by an agent. 

A value function can be used to derive a policy by selecting actions that maximize the expected long-term reward at each state in the policy. On the other hand, a policy may also be learned directly without first computing a value function. This is known as policy optimization.

In general, using a value function to guide policy selection is a popular and effective approach for many reinforcement learning problems. However, for high-dimensional or continuous state-action spaces, policy optimization may be more computationally efficient and scalable. 

Ultimately, the choice between using a value-function-based or policy optimization approach will depend on the specifics of the reinforcement learning problem and the computational resources available.
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