Questions tagged [notation]
For questions related to notation (in general).
3
votes
1answer
29 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
53 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, ...
1
vote
2answers
60 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....
3
votes
0answers
43 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
42 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
93 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 ...
1
vote
1answer
188 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 ...
