RNNs can count with infinite compute bounds. Realistically, this isn't feasible, but papers have suggested that LSTMs can learn to count. I explore some of the basic principles around an LSTM counting.
There seem to be certain heuristic strategies people suggest for winning the game 2048. I wanted to explore why these policies work with reinforcement learning