The Bellman equation is a fundamental concept in dynamic programming and reinforcement learning, named after Richard Bellman. It describes the relationship between the value of a decision and the value of future decisions in a given state. The equation provides a recursive way to compute the optimal policy and the value function for a Markov Decision Process (MDP).
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