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Bellman Equations, Dynamic Programming, Generalized Policy Iteration | Reinforcement Learning Part 2

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This course breaks down how Bellman equations, dynamic programming, and generalized policy iteration work together to compute optimal policies and value functions in reinforcement learning, using examples like the gambler’s problem to show their power and limits.

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Best quote from Bellman Equations, Dynamic Programming, Generalized Policy Iteration | Reinforcement Learning Part 2

The Bellman equations provide the connecting web of our state space, a space that our agent needs to intelligently traverse. As we'll see, these equations allow us to convert information about the environment into improvements in the agent's behavior.

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