How to find the best exploration parameter in a Monte Carlo tree search?
The  value  of $C = \sqrt 2$  was  shown to ensure the asymptotic optimality when rewards are in the $[0,1]$ range (Kocsis, Szepesvári, 2006).

In many games, that reward range is straightforward: maximum and minimum possible scores can be translated to 0 and 1 (0 could mean a loss and 1 a win).

The accuracy of this squashing seems to have a minimal impact (e.g. Using Domain Knowledge to Improve Monte-Carlo Tree Search Performance in Parameterized Poker Squares - Robert Arrington, Clay Langley, and Steven Bogaerts - 2016).

Unfortunately squashing isn't always possible.

With rewards outside the $[0, 1]$ range and/or for fine-tuning the Cross Entropy Method works quite well.