Answers.Body: <p>Since no one answers, I will say the approach I used.
  It was inspired by the successive halving algorithm. The concept is that we find good performing hyperparameters for one agent by playing manually against it. Then, we put the agent we want to tune to play against it. We assign different configurations. All of these configurations are tried for a predefined number of games per configuration. We keep the best performing half, and then go to the next round with the half we kept and repeat this process. The last configuration that remains is the best performing configuration.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-04-29T11:29:34.627Z
Answers.LastActivityDate: 2020-04-29T11:29:34.627Z
Answers.UserDisplayName: paulaxa1
Body: <p>I have a basic MCTS agent for the game of Hex (a turn based game). I want to tune the parameters of UCT (the Cp parameter) and the number of rollouts parameter.</p>
  
  <p>Where do I have to begin? The problem is that the agent is smart enough to always win if it plays first against another agent. So I don't know how to do the evaluation of each pair of hyperparameters.</p>
  
  <p>If anyone has any ideas let me know.</p>
  
Tags: <reinforcement-learning><monte-carlo-tree-search><monte-carlo-methods>
Title: How to apply hyperparameter optimization on Monte Carlo Tree Search?
UserDisplayName: paulaxa1
ThreadText: # How to apply hyperparameter optimization on Monte Carlo Tree Search?
  
  (Asked by: username_0 on 2020-03-15)
  
  I have a basic MCTS agent for the game of Hex (a turn based game). I want to tune the parameters of UCT (the Cp parameter) and the number of rollouts parameter.
  
  Where do I have to begin? The problem is that the agent is smart enough to always win if it plays first against another agent. So I don't know how to do the evaluation of each pair of hyperparameters.
  
  If anyone has any ideas let me know.
  
  **Tags:** reinforcement-learning, monte-carlo-tree-search, monte-carlo-methods
  
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  ## 1 answers
  
  ### Answer by username_0 on 2020-04-29. Score: 0 | Accepted answer
  
  Since no one answers, I will say the approach I used.
  It was inspired by the successive halving algorithm. The concept is that we find good performing hyperparameters for one agent by playing manually against it. Then, we put the agent we want to tune to play against it. We assign different configurations. All of these configurations are tried for a predefined number of games per configuration. We keep the best performing half, and then go to the next round with the half we kept and repeat this process. The last configuration that remains is the best performing configuration.
  
  
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