  In recent years, much progress has been made in computer Go and most of the
results have been obtained thanks to search algorithms (Monte Carlo Tree
Search) and Deep Reinforcement Learning (DRL). In this paper, we propose to use
and analyze the latest algorithms that use search and DRL (AlphaZero and
Descent algorithms) to automatically learn to play an extended version of the
game of Go with more than two players. We show that using search and DRL we
were able to improve the level of play, even though there are more than two
players.
