Every variant of Monte Carlo Tree Search faces the explore-exploit tradeoff: pick the branch that looks best right now, or test new branches? Algorithms like PUCT, used in AlphaGo, score each move with two competing terms. One is how good a move looks based on your exploration up till now. The other is a novelty bonus that rewards moves you've not visited much. The neat thing is that, over time, the term dominating the overall score shifts automatically. The algorithm hands off from…