Questions tagged [notation]
For questions related to notation (in general).
23
questions
1
vote
1answer
79 views
What does the notation $\partial \theta_{\pi}$ mean in this actor-critic update rule?
One of the steps in the actor-critic algorithm is $$\partial \theta_{\pi} \gets \partial \theta_{\pi} + \nabla_{\theta}\log\pi_{\theta} (a_i | s_i) (R - V_{\theta}(s_i))$$
For me, $\theta$ are just ...
1
vote
1answer
34 views
What does equation in the “related work” section of the GAN paper mean?
I was going through the paper on GAN by Ian Goodfellow. Under the related work section, there is an equation. I cannot decipher the equation. Can anyone help me understand the meaning of the equation? ...
2
votes
1answer
29 views
What does the notation ${s'\sim T(s,a,\cdot)}$ mean?
I have been seeing notations on Expectations with their respective subscripts such as $E_{s_0 \sim D}[V^\pi (s_0)] = \Sigma_{t=0}^\infty[\gamma^t\phi(s_t)]$. This equation is taken from https://ai....
0
votes
1answer
32 views
What is the purpose of the arrow $\leftarrow$ in this formula?
What is the purpose of the arrow $\leftarrow$ in the formula below?
$$V(S_t) \leftarrow V(S_t) + \alpha \left[ G_t - V(S_t) \right]$$
I presume it's not the same as 'equals'.
1
vote
1answer
31 views
Why is exp used in encoder of VAE instead of using the value of standard deviation alone?
There's one VAE example here:
https://towardsdatascience.com/teaching-a-variational-autoencoder-vae-to-draw-mnist-characters-978675c95776.
And the source code of encoder can be found at the ...
0
votes
1answer
34 views
How to understand the average l2 loss?
In the snippet below, the highlighted part is the average norm, but since $1/|p_i|$ is outside the summation, it is very confusing to understand.
is $|p_i|$ l2-norm(as per wolfram) or l1-norm or ...
3
votes
1answer
81 views
What is the difference between the notations $\|x\|_1, \|x\|_2$ and $|x|$?
What is the difference between the notations $\|x\|_1, \|x\|_2$ and $|x|$? I think $|x|$ is the magnitude of $x$.
3
votes
1answer
51 views
What does the notation sup dist mean in distributional RL?
I'm trying to understand distributional RL, based on this article. In one of the equations, there is a symbol $\operatorname{sup dist}$.
\begin{align}
\operatorname{sup dist}_{s, a} (R(s, a) + \...
0
votes
0answers
17 views
How is the general return-based off-policy equation derived?
I'm wondering how is the general return-based off-policy equation in Safe and efficient off-policy reinforcement learning derived
$$\mathcal{R} Q(x, a):=Q(x, a)+\mathbb{E}_{\mu}\left[\sum_{t \geq 0} \...
4
votes
1answer
57 views
What do the subscripts mean in $N_{t,n,\sigma,L}$?
A neural network can apparently be denoted as $N_{t,n,\sigma,L}$. What do these subscripts $t, n, \sigma$ and $L$ mean? Could you link me to a paper, article or webpage with an explanation for this?
3
votes
1answer
49 views
Sutton & Barto's notation $V_{t+n}$ in Chapter 7: $n$-step Bootstrapping
Until Chapter 6 of Sutton & Barto's book on Reinforcement Learning, the authors use $V$ for the current estimate of a state value. Equation (6.1), for example, shows:
$$ V(S_t) \leftarrow V(S_t) +...
4
votes
1answer
58 views
What is the meaning of the square brackets in ant colony optimization?
I'm studying the paper "Minimizing Total Tardiness on a Single Machine Using Ant Colony Optimization" which has proposed to use Ant colony optimization to SMTWTP.
According to this paper:
Each ...
2
votes
1answer
24 views
Understanding the equation of the empirical error
The empirical error equation given in the book Understanding Machine Learning: From Theory to Algorithms is
My intuition for this equation is: total wrong predictions divided by the total number of ...
5
votes
1answer
309 views
Understanding the equation of TD(0) in the paper “Learning to predict by the methods of temporal differences”
In the paper Learning to predict by the methods of temporal differences (p. 15), the weights in the temporal difference learning are updated as given by the equation
$$
\Delta w_t
= \alpha \left(P_{t+...
3
votes
1answer
40 views
Being confused of distribution notations in Deep Learning book
In chapter 5 of Deep Learning book of Ian Goodfellow, some notations in the
loss function as below make me really confused.
I tried to understand $x,y \sim p_{data}$ means a sample $(x, y)$ sampled ...
4
votes
1answer
297 views
How is the policy gradient calculated in REINFORCE?
Reading Sutton and Barto, I see the following in describing policy gradients:
How is the gradient calculated with respect to an action (taken at time t)? I've read implementations of the algorithm, ...
2
votes
3answers
102 views
What does the formula $1-\sum_i(e_i-a_i)^2$ mean in this NEAT Python API?
I have looked at the documentation for the NEAT Python API found here, but it shows calculus like this:
The error for each genome is $1-\sum_i(e_i-a_i)^2$
I haven't learned calculus at the moment....
7
votes
1answer
461 views
Understanding notation of Goodfellow's GAN objective function
What is the meaning of $V(D,G)$? How do we get these expectation parts?
I was trying to understand it following this article: Understanding Generative Adversarial Networks (D.Seita), but, after many ...
2
votes
1answer
48 views
What is the use of the $\epsilon$ term in this back-propagation equation?
I am currently looking at different documents to understand back-propagation, mainly at this document. Now, at page 3, there is the $\epsilon$ symbol involved:
While I understand the main part of the ...
2
votes
1answer
113 views
Understanding the notation in the definition of the expected reward
I am new to RL and I am trying to work through the book Reinforcement Learning: An Introduction I (Sutton & Barto, 2018). In chapter 3 on Finite Markov Decision Processes, the authors write the ...
2
votes
1answer
76 views
What does the notation $\nabla_\theta \mathcal{L}$ mean?
Here's the general algorithm of maximum entropy inverse reinforcement learning.
This uses a gradient descent algorithm. The point that I do not understand is there is only a single gradient value $\...
3
votes
2answers
245 views
What does the argmax of the expectation of the log likelihood mean?
What does the following equation mean? What does each part of the formula represent or mean?
$$\theta^* = \underset {\theta}{\arg \max} \Bbb E_{x \sim p_{data}} \log {p_{model}(x|\theta) }$$
1
vote
1answer
195 views
What is $I$ in the noise described in the paper “Parameter Space Noise for Exploration”?
In the paper Parameter Space Noise for Exploration, the authors describe the noise that they add to the parameter vector as:
$$
\tilde{\theta} = \theta + \mathcal{N}(0, \sigma^2I)
$$
is $I$ simply ...
