Hey Frank,
training worked, however the result is not so good the mcts trained player is only a little bit better than a random player.
I think the next step is to go to real learning. However for this multiplayer game learning is not so easy, as action space can vary depending on the cards. Moreover cards of other players are not known (imperfect information). For this reason I would try a Policy Gradient Method to learn in such a case.
What would you suggest? The only similar example I found is here, which is not documented at all 
You can checkout the game here:

  
      
      GitHub
  
  
    

CesMak/mcts_cardgame 3

Tests with Monte Carlo Tree Search on a multi player card game - CesMak/mcts_cardgame