From Wikipedia, in the Monte-Carlo Tree Search algorithm, you should choose the node that maximizes the value:
$${\displaystyle {\frac {w_{i}}{n_{i}}}+c{\sqrt {\frac {\ln N_{i}}{n_{i}}}}},$$
where
${w_{i}}$ stands for the number of wins for the node considered after the $i$-th move,
${n_{i}}$ stands for the number of simulations for the node considered after the $i$-th move,
$N_{i}$ stands for the total number of simulations after the $i$-th move run by the parent node of the one considered
$c$ is the exploration parameter—theoretically equal to$\sqrt{2}$; in practice usually chosen empirically.
Here (and I've seen in other places as well) it claims that the theoretical ideal value for $c$ is $\sqrt{2}$. Where does this value come from?
(Note: I did post this same question on cross-validated before I knew about this (more relevant) site)
