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en:dydaktyka:ggp:game_tree_2 [2019/01/08 19:40]
msl Translates to english
en:dydaktyka:ggp:game_tree_2 [2019/01/08 23:19]
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 Monte Carlo wasn't the smartest approach to the problem - we can be more intelligent than some random sampling. The idea behind a more advanced Monte Carlo Tree Search is to choose moves, according to a simple scoring formula: Monte Carlo wasn't the smartest approach to the problem - we can be more intelligent than some random sampling. The idea behind a more advanced Monte Carlo Tree Search is to choose moves, according to a simple scoring formula:
  
-$UCT = \overline{score_i} + c*\sqrt{{n}\over{n_i}$+$UCT = \overline{score_i} + c*\sqrt{{n}\over{n_i} $
  
 where: where:
-  * $\overline{score_i}$ the average score of the $i$-th move +  * $\overline{score_i} $ the average score of the $i $-th move 
-  * $n$ is total number of playouts +  * $n $ is total number of playouts 
-  * $n_i$ is number of playouts after choosing the $i$-th move+  * $n_i $ is number of playouts after choosing the $i $-th move
   * $c$ is a constant parameter, from theoretical reasons set traditionally to $\sqrt{2}$   * $c$ is a constant parameter, from theoretical reasons set traditionally to $\sqrt{2}$
  
 As you see, the UCT assigns high scores to machines that: As you see, the UCT assigns high scores to machines that:
-  * have high average score ($\overline{score_i}$ is high)+  * have high average score ($\overline{score_i} $ is high)
   * haven'​t been tried too many times (low $\over{n_i}$).   * haven'​t been tried too many times (low $\over{n_i}$).
  
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   - gray moves belong to our opponent, so during backpropagation we store our loss as his win.    - gray moves belong to our opponent, so during backpropagation we store our loss as his win. 
  
-{{:​en:​dydaktyka:​ggp:​mcts-phases.png | https://​en.wikipedia.org/​wiki/​Monte_Carlo_tree_search#/​media/​File:​MCTS_(English)_-_Updated_2017-11-19.svg}}+{{:​en:​dydaktyka:​ggp:​mcts-phases.png?700 | https://​en.wikipedia.org/​wiki/​Monte_Carlo_tree_search#/​media/​File:​MCTS_(English)_-_Updated_2017-11-19.svg}}
  
  
en/dydaktyka/ggp/game_tree_2.txt · Last modified: 2019/01/08 23:19 by msl
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