3
votes
Accepted
Is value iteration stopped after one update of each state?
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 ...
3
votes
Can you provide some pseudocode examples of what constitutes an AI?
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", ...
2
votes
Accepted
Does a differential evolution algorithm mutate its population during a generation?
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 ...
2
votes
Can you provide some pseudocode examples of what constitutes an AI?
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 ...
2
votes
Accepted
Understanding the pseudocode of uniform-cost search from the book "Artificial Intelligence: A Modern Approach"
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:
...
2
votes
Pseudocode for CNN with Bounding Box and Classifier
The minimal algorithm for convolution in $\mathbb{R}^2$ is a four dimensional iteration.
...
2
votes
Why does this Keras implementation of the DDPG algorithm update the critic's network using the gradient but the pseudocode doesn't?
The answer to your first question is because the line 'update the critic by minimising the loss $L = \frac{1}{N} \sum_i \left( y_i - Q(s_i, a_i |\theta^Q)\right)^2$ is implying that you will do this ...
2
votes
In the DQN paper, why do we have both $\max_{a'}$ and $\max_{a}$ in the pseudocode?
$r_j + \gamma \max_{a'}Q(\phi_{j+1},a';\theta)$
I'm confused as to what $a'$ refers to and where it comes from.
Here $a'$ is a "dummy" argument over which you perform the maximization ...
1
vote
Why don't we bootstrap terminal state in n-step temporal difference prediction update equation?
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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