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I know that the mathematical justification for using the Bellman-equation iteratively to find the optimal policy in Reinforcement Learning is based on convergence results. I wonder however whether there is a mathematical intuition for this convergence. In one of his lectures on Youtube, David Silver mentioned that this is some sort of contraction argument and I wondered whether this can be made intuitive.

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