Zero Padding in Convolutional neural network We use Convolutional neural network because it by design learns features that generalize over spatial location , so when using conv operation it reduce image size and that what we hope to have so we can have more simplicity in the following layers , but when doing zero padding the image return to its original size , so what is the benefit of doing this ? the same problem return back since image size will be still large . Also, CNN works well for classify images; so how it can be used in games to detect best next move, I mean how it decide the Q values of the actions from the image itself even though understanding image doesn't give any indication of the next move  ?
For padding in CNN's there is a useful answer in this link:https://stats.stackexchange.com/questions/246512/convolutional-layers-to-pad-or-not-to-pad
In Q-learning an action is taken in current state, and a next state is obtained. Then state-action value of the current state-action pair is updated by using best state-action value of obtained next state. If you have discrete state-action space, or a few states you can simply record each state-action pair in a table, and then update it by Q-learning, but if the space is continuous, or there are so many state-action pairs then you should use function approximation methods like neural networks. For example by extracting current state's visual features by CNN, and then updating weights to better approximate value of this perceived state, and taken action pair.