The Bellman operator plays a crucial role in reinforcement learning, as it provides a way to compute the value function recursively. This operator's convergence assures that the approximations of the value function will reach a stable solution, facilitating effective decision-making within uncertain environments.
In simplifying the proofs for Theorems 3 and 4, we highlight the significance of understanding how the Bellman operator behaves. By revealing the underlying structures and convergence properties, newcomers can begin to grasp complex concepts without being overwhelmed.
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