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Where the author mentions the policy evaluation being stopped after one state, they are referring to the part of the algorithm that evaluates the policy -- the pseudocode you have listed is the pseudocode for Value Iteration, which consists of iterating between policy evaluation and policy improvement. In normal policy evaluation, you would apply the update $...


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Philosophically, my own research has led me to understand AI as any artifact that makes a decision. This is because the etymology of "intelligence" strongly implies "selecting between alternatives", and these meanings are baked in all the way back to the proto-Indo-European. (Degree of intelligence, or "strength" is merely a measure of utility, typically ...


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AI is not a simple term. There are different types, ranging from the most simplistic rule-based AI to black-box AI's so complicated it's unreasonable for a human to understand exactly what they're doing. There's no pseudocode that if used in a program automatically constitutes it as an AI. It's not that black and white. But I can give examples: Here's a ...


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I think this is a problem with missing brackets in pseudocode — clearly the state is only added to the frontier if it hasn't been explored already, so it would be: if not [contains(frontier, state) OR contains(explored, state)] then which is equivalent to your interpretation of if not [contains(frontier, state)] AND not [contains(explored, state)] ...


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Quoting the original paper: For each target vector $x_{i,G}$ ,a mutant vector is generated according to $$ v_{i,G+1} = x_{r_1,G} + F\left(x_{r_2,G} + x_{r_3,G}\right)$$ And later To decide whether or not it should become a member of generation $G + 1$, the trial vector $v_{i,G+1}$ is compared to the target vector $x_{i,G}$ using the greedy criterion. I'd ...


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Because the value of the terminal state is 0 by definition. There is no further reward to be obtained once you reach the terminal state.


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