Answers.Body: <p>On page 13, right under Table S1 in the linked paper, this is explained (emphasis in bold at the end mine):</p>
  
  <blockquote>
    <p>Each set of planes represents the board position at a time-step <span class="math-container">$t - T + 1, \dots, t$</span>, <strong>and is set to zero for time-steps less than <span class="math-container">$1$</span>.</strong></p>
  </blockquote>
  
  <hr>
  
  <p>I suspect the solution they write there would indeed work better than just repeating the starting position up to 8 times. Intuitively, you'll want the Neural Network to learn to primarily focus on the current game state. If the starting position is repeated a bunch of times in those planes, the Neural Network cannot distinguish between any of them in the learning process in the first few steps, and may start relying on them all equally. Only in later time steps will it "figure out" that they're sometimes not equal, and that the last one is probably the most informative one. If the "useless planes" during the first few steps are all-zero, they can be much more easily ignored in the start of the learning process.</p>
  
  <p>Note that I suspect the difference really won't matter a whole lot at all, I suspect there'd just be a tiny difference in learning speed based on the intuition described above.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-11-23T16:00:43.560Z
Answers.LastActivityDate: 2018-11-23T16:00:43.560Z
Answers.UserDisplayName: Dennis Soemers
Body: <p><a href="https://arxiv.org/pdf/1712.01815.pdf" rel="nofollow noreferrer">The Alpha zero paper</a> says (in the caption of Table S1) that</p>
  <blockquote>
  <p>The first set of features are repeated for each position in a <span class="math-container">$T = 8$</span>-step history.</p>
  </blockquote>
  <p>So, what happens before the first 8 moves? Do they just repeat the starting position?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-01-02T20:25:22.637Z
Comments.Text: I guess they just use a database of chess opening moves. These exist since the 1970, and fits in a few gigabytes of  disk file memory. See also http://refpersys.org/ and contact me by email to basile@starynkevitch.net
Comments.UserDisplayName: Basile Starynkevitch
Tags: <papers><alphazero>
Title: What happens before the first 8 moves in Alpha Zero?
UserDisplayName: Bojidar Ivanov
ThreadText: # What happens before the first 8 moves in Alpha Zero?
  
  (Asked by: username_0 on 2018-11-23)
  
  [The Alpha zero paper](https://arxiv.org/pdf/1712.01815.pdf) says (in the caption of Table S1) that
  
  > The first set of features are repeated for each position in a $T = 8$-step history.
  
  So, what happens before the first 8 moves? Do they just repeat the starting position?
  
  **Tags:** papers, alphazero
  
  > **Comments:**
  > **username_1**: I guess they just use a database of chess opening moves. These exist since the 1970, and fits in a few gigabytes of  disk file memory. See also http://refpersys.org/ and contact me by email to username_1@username_1.net  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2018-11-23. Score: 4 | Accepted answer
  
  On page 13, right under Table S1 in the linked paper, this is explained (emphasis in bold at the end mine):
  
  > Each set of planes represents the board position at a time-step $t - T + 1, \dots, t$, **and is set to zero for time-steps less than $1$.**
  
  ---
  
  I suspect the solution they write there would indeed work better than just repeating the starting position up to 8 times. Intuitively, you'll want the Neural Network to learn to primarily focus on the current game state. If the starting position is repeated a bunch of times in those planes, the Neural Network cannot distinguish between any of them in the learning process in the first few steps, and may start relying on them all equally. Only in later time steps will it "figure out" that they're sometimes not equal, and that the last one is probably the most informative one. If the "useless planes" during the first few steps are all-zero, they can be much more easily ignored in the start of the learning process.
  
  Note that I suspect the difference really won't matter a whole lot at all, I suspect there'd just be a tiny difference in learning speed based on the intuition described above.
  
  
  ---
  
  