Simulating 38K games to find optimal strategy is the kind of overkill analysis that makes HN great. There's something deeply satisfying about applying brute-force computation to a casual word game.
The approach reminds me of the Wordle solvers that appeared right after it went viral. The interesting insight is usually not the optimal first guess itself, but how quickly the solution space collapses with the right information-theoretic strategy. Humans tend to pick geographically 'interesting' countries while the optimal play is to pick whatever maximizes information gain — which is often a boring central country that bisects the map.
I reverse-engineered Countryle and simulated 38,612 games to find the best strategy. Using entropy and geographic data, I discovered the best starting country and built a bot that solves the game in 2.85 guesses on average.
I wonder how the possible positions of this game would look graphed like this .. https://2swap.github.io/Klotski-Webpage/
Simulating 38K games to find optimal strategy is the kind of overkill analysis that makes HN great. There's something deeply satisfying about applying brute-force computation to a casual word game.
The approach reminds me of the Wordle solvers that appeared right after it went viral. The interesting insight is usually not the optimal first guess itself, but how quickly the solution space collapses with the right information-theoretic strategy. Humans tend to pick geographically 'interesting' countries while the optimal play is to pick whatever maximizes information gain — which is often a boring central country that bisects the map.
I reverse-engineered Countryle and simulated 38,612 games to find the best strategy. Using entropy and geographic data, I discovered the best starting country and built a bot that solves the game in 2.85 guesses on average.
Nice work! What is the max amount of guesses?
Nice read, but it seems that his headline is wrong because he still has room for improvement.
Great read! Thanks for sharing.