Answers.Body: <p>Your intuition is right. The tree is grown after each game. </p>
  
  <p>Before the first play through, the values of each decision point are initialized (randomly or to some constant). Then at the end of one play through, the weights of the decision points are updated using Monte Carlo methods. In this way, on the next play through, the updated weights help the agent make a decision in the next game. </p>
  
  <p>As for how the decision points are added to the tree, it depends on the application. For simple games like TicTacToe, we know all of the decision points. For big games, there are methods to trim the tree of bad performing branches. </p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-10-24T14:21:55.303Z
Answers.LastActivityDate: 2017-10-24T14:21:55.303Z
Answers.UserDisplayName: Jaden Travnik
Body: <p>AIs that rely on MCTS - like AlphaGo - create their decision tree as the game progresses. Do they start from scratch each game and build a new tree or do they keep the tree and grow it from game to game further?</p>
  
  <p>Besides the possible limitations of storage space for the search tree, I don't see any obvious drawback in keeping and growing the tree, which seems to me to be the preferred option. Are there other reasons to start from scratch each game?</p>
  
Tags: <ai-design><monte-carlo-tree-search>
Title: Do AIs based on MCTS start each game from scratch?
UserDisplayName: Demento
ThreadText: # Do AIs based on MCTS start each game from scratch?
  
  (Asked by: username_0 on 2017-10-22)
  
  AIs that rely on MCTS - like AlphaGo - create their decision tree as the game progresses. Do they start from scratch each game and build a new tree or do they keep the tree and grow it from game to game further?
  
  Besides the possible limitations of storage space for the search tree, I don't see any obvious drawback in keeping and growing the tree, which seems to me to be the preferred option. Are there other reasons to start from scratch each game?
  
  **Tags:** ai-design, monte-carlo-tree-search
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2017-10-24. Score: 3 | Accepted answer
  
  Your intuition is right. The tree is grown after each game.
  
  Before the first play through, the values of each decision point are initialized (randomly or to some constant). Then at the end of one play through, the weights of the decision points are updated using Monte Carlo methods. In this way, on the next play through, the updated weights help the agent make a decision in the next game.
  
  As for how the decision points are added to the tree, it depends on the application. For simple games like TicTacToe, we know all of the decision points. For big games, there are methods to trim the tree of bad performing branches.
  
  
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