The first step of MCTS is to keep choosing nodes based on Upper Confidence Bound applied to trees (UCT) until it reaches a leaf node where UCT is defined as
$$\frac{w_i}{n_i}+c\sqrt{\frac{ln(t)}{n_i}},$$
where
- $w_i$= number of wins after i-th move
- $n_i$ = number of simulations after the i-th move
- $c$ = exploration parameter (theoretically equal to $\sqrt{2}$)
- $t$ = total number of simulations for the parent node
I don't really understand how this equation avoids sibling nodes being starved, aka not explored. Because, let's say you have 3 nodes, and 1 we'll call it node A is chosen randomly to be explored, and just so happens to simulate a win. So, node A's UCT$=1+\sqrt(2)\sqrt{\frac{ln(1)}{1}}$, while the other 2 nodes UCT = 0, because they are unexplored and the game just started, so by UCT the other 2 nodes will never be explored no? Because after this it'll go into the expansion phase and expansion only happens it reaches a leaf node in the graph. So because node A is the only one with a UCT $> 0$ it'll choose a child of node A and it will keep going down that node cause all the siblings of node A have a UCT of 0 so they never get explored.