Problem 3. (15 points) Assume you are running Monte-Carlo tree search on a binary tree (i.e. branching factor of 2 everywhere). Also assume on ties you pick the left-most tied node. You see the following sequence of wins/losses from the random rollout": Win, Win, Loss, Loss, Win, Win, Loss, Win, Win Show the tree and all relevant UCB (upper confidence bound) values for making the next decision on which node to pick at the indicated (i.e. numbered) points in running the algorithm (same sequence of win/loss as above): Win, Win, Loss, (1), Loss, Win, (2), Win, Loss, Win, Win, (3) Problem 3. (15 points) Assume you are running Monte-Carlo tree search on a binary tree (i.e. branching factor of 2 everywhere). Also assume on ties you pick the left-most tied node. You see the following sequence of wins/losses from the random rollout": Win, Win, Loss, Loss, Win, Win, Loss, Win, Win Show the tree and all relevant UCB (upper confidence bound) values for making the next decision on which node to pick at the indicated (i.e. numbered) points in running the algorithm (same sequence of win/loss as above): Win, Win, Loss, (1), Loss, Win, (2), Win, Loss, Win, Win, (3)