Reproduction and Extension of Playing Strength Models in Computer Go

Monte Carlo Tree Search (MCTS) is a well established approach for computer players to the game of Go as well as other combinatorial problems. To improve the playing strength of the algorithm, it can be run for a longer time or be executed in parallel. The traditionally suggested regression model for the achievable playing strength with MCTS uses an exponential decay and nicely matches the observations of other authors as well as our own replication. However, it implies a rather low upper bound, even for non-parallel execution. This appears to disagree with the property of MCTS to asymptotically converge towards perfect play, which should still be far from achievable. Yet, to our knowledge, there has been neither an explicit verification of these findings nor an attempt to explain the observations differently. Here, we look into the real-world playing strength of a state-of-the-art Go program, Katago, in various environments. We find that there is at least one alternative model of very simple nature that does not impose an upper bound while matching the observations to a similar degree.