  Tic Tac Toe is amongst the most well-known games. It has already been shown
that it is a biased game, giving more chances to win for the first player
leaving only a draw or a loss as possibilities for the opponent, assuming both
the players play optimally. Thus on average majority of the games played result
in a draw. The majority of the latest research on how to solve a tic tac toe
board state employs strategies such as Genetic Algorithms, Neural Networks,
Co-Evolution, and Evolutionary Programming. But these approaches deal with a
trivial board state of 3X3 and very little research has been done for a
generalized algorithm to solve 4X4,5X5,6X6 and many higher states. Even though
an algorithm exists which is Min-Max but it takes a lot of time in coming up
with an ideal move due to its recursive nature of implementation. A Sample has
been created on this link \url{https://bk-tic-tac-toe.herokuapp.com/} to prove
this fact. This is the main problem that this study is aimed at solving i.e
providing a generalized algorithm(Approximate method, Learning-Based) for
higher board states of tic tac toe to make precise moves in a short period.
Also, the code changes needed to accommodate higher board states will be
nominal. The idea is to pose the tic tac toe game as a well-posed learning
problem. The study and its results are promising, giving a high win to draw
ratio with each epoch of training. This study could also be encouraging for
other researchers to apply the same algorithm to other similar board games like
Minesweeper, Chess, and GO for finding efficient strategies and comparing the
results.
