What layers to use in a Neural Network for card game I am currently writing an engine to play a card game and I would like for an ANN to learn how to play the game. The game is currently playable, and I believe for this game a deep-recurrent-Q-network with a reinforcement learning approach is the way to go. 

However, I don't know what type of layers I should use, I found some examples of Atari games solved through ANN, but their layers are CNN (convolutional), which are better for image processing. I don't have an image to feed the NN, only a state composed of a tensor with cards in the player's own hand and cards on the table. And the output of the NN should be a card or the action 'End Turn'.

I'm currently trying to use TensorFlow but I'm open to any library that can work with NN. Any type of help or suggestion would be greatly appreciated!
The game state consists of the location of all the hidden cards, so you probably need a softmax layer, 52*n, where n is the number of locations.

I'm not very sure that a NN is a good match.