I failed using PPO to train a multiplayer card game. Thus I tested monte carlo tree search (mcts) to predict good moves. This works now (you can test the game here. As calculating a good move using mcts takes quite long, I wondered if I could use the predicted good moves from mcts and the input state to train a neuronal network.

I would use the following to train the neuronal network:

  • Input State [played cards, cards on table, cards in the hand of the player] 60 Cards in game thus this is a 180x1 binary vector
  • Output State [60x1 vector of the best predicted move from mcts, 4x1 rewards vector of the 4 players]

What I basically would like to achieve is a Neuronal Network that would predict me the action I should play and the estimated output rewards at the end of the game for a given Input State.

Whats Data should I generate to efficiently train the network?

  • Should I just use the good moves mcts is predicting or should I use also the bad ones?
  • Should I setup the game players to be all mcts / or random players?
  • Is it possible that the Network does learn anything at all from this data?

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