Hey there,
I currently used Monte Carlo Tree Search (MCTS) to predict good actions for a card game (4 players, each 15 cards). This works quite nice, but is computationally expensive. That is why I thought about training a Neuronal Network with that data.
My goal is that this nn should predict me very fast an action for an input state vector.
My data (generated by MCTS) for one batch is as follows:

x: input vector: 180x1

60x1 binary vector for card is on the table
60x1 binary vector for card is already played
60x1 binary vector for card is on the players hand.
*y: output vector: 60x1 (has only one 1 for the card index of the action that is played by the MCTS player).



Questions:

As you can see I have binary input and binary output vector. Is this advisable to use binary inputs and binary outputs?
I have no doubt on how to design my nn (number of hidden layers, relu or linear etc.) What would you suggest?
Also the loss function I do not know how I should choose it.

I currently searched on classifier examples but I mostly found classifiers for images and also in linear case (no binary inputs / outpus)
Hope you can point me to some examples which suit my case.