Neural Network Agent

This agent simply takes current state as an input and predict the best possible move which can make agent win. 

Neural Network is made up of 4 hidden layers including one input layer and one output layer. Each layer is having 26 neurons in it and output layer have 7 neurons. This 7 neurons represents columns of a board and Neural network gives best possible column to play move.  Softmax_cross_entropy is used for cost estimation and AdamOptimizer for optimization of cost of neural network while back propagating. Neural Network is trained using random rollout agent played against another one with different walks. Model is trained on 150,000 games which are won by AI agent.

This neural network returns vector of size 7 which contain probability values for each column and we can select highest one to make it closer to win.