Bellman Equation

Bellman equation is a recursive mathematical equation used in dynamic programming and reinforcement learning to calculate the optimal value of a decision at a given state, by considering the expected reward of all possible actions that can be taken from that state and the expected value of the next state. The equation is named after Richard Bellman, who introduced it in the 1950s. Bellman equation is widely used in artificial intelligence and machine learning algorithms, particularly in the field of reinforcement learning, to find the optimal policy for an agent to take actions in an environment. It is a fundamental concept in Markov decision processes and dynamic programming.
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