# Tag Info

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 $... 2 Philosophically, my own research has led me to understand AI as any artifact that makes a decision. This is because the etymology of "intelligence" strongly implies "selecting between alternatives", and these meanings are baked in all the way back to the proto-Indo-European. (Degree of intelligence, or "strength" is merely a measure of utility, typically ... 2 AI is not a simple term. There are different types, ranging from the most simplistic rule-based AI to black-box AI's so complicated it's unreasonable for a human to understand exactly what they're doing. There's no pseudocode that if used in a program automatically constitutes it as an AI. It's not that black and white. But I can give examples: Here's a ... 2 I think this is a problem with missing brackets in pseudocode — clearly the state is only added to the frontier if it hasn't been explored already, so it would be: if not [contains(frontier, state) OR contains(explored, state)] then which is equivalent to your interpretation of if not [contains(frontier, state)] AND not [contains(explored, state)] ... 2 The minimal algorithm for convolution in$\mathbb{R}^2$is a four dimensional iteration. for all vertical kernel positions for all horizontal kernel positions initialize the value at the output position to the bias for all vertical positions in the kernel for all horizontal positions in the kernel add the product of the input value ... 2 Quoting the original paper: For each target vector$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 ...