  In the past few years, AlphaZero's exceptional capability in mastering
intricate board games has garnered considerable interest. Initially designed
for the game of Go, this revolutionary algorithm merges deep learning
techniques with the Monte Carlo tree search (MCTS) to surpass earlier top-tier
methods. In our study, we broaden the use of AlphaZero to Gomoku, an age-old
tactical board game also referred to as "Five in a Row." Intriguingly, Gomoku
has innate challenges due to a bias towards the initial player, who has a
theoretical advantage. To add value, we strive for a balanced game-play. Our
tests demonstrate AlphaZero's versatility in adapting to games other than Go.
MCTS has become a predominant algorithm for decision processes in intricate
scenarios, especially board games. MCTS creates a search tree by examining
potential future actions and uses random sampling to predict possible results.
By leveraging the best of both worlds, the AlphaZero technique fuses deep
learning from Reinforcement Learning with the balancing act of MCTS,
establishing a fresh standard in game-playing AI. Its triumph is notably
evident in board games such as Go, chess, and shogi.

Artificial Intelligence;Machine Learning