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This code is well-organized for figuring out the best strategies in the game of Nim. The functions are clear and separated, making it easy to follow. However, it might be helpful to add comments in English to make it even clearer.
The peer did an impressive job coming up with six different strategies. They tested each of these strategies by playing 100 games for every move to figure out which one works the best. Even though the win rate is around 50% and there's a suggestion to use a different method called "nim_sum" for testing, I think it's a good call by the peer to explore other Evolutionary Strategies (ES) that are more adaptive. This makes sense because relying solely on "nim_sum" might not always be the best approach, especially in different game scenarios. The peer's effort in trying different strategies and seeking adaptability is a positive aspect of their work.
One thing to note is that the code runs a bit slowly because of the many games played for each move in the "adaptive" strategy. Despite this, it's understandable considering the thorough evaluation process.
One small improvement could be adding a metric for the percentage of victories. Despite this suggestion, the code already prints the number of victories for both players after each set of 100 games, providing useful information about how well the strategies are performing.
This code is well-organized for figuring out the best strategies in the game of Nim. The functions are clear and separated, making it easy to follow. However, it might be helpful to add comments in English to make it even clearer.
The peer did an impressive job coming up with six different strategies. They tested each of these strategies by playing 100 games for every move to figure out which one works the best. Even though the win rate is around 50% and there's a suggestion to use a different method called "nim_sum" for testing, I think it's a good call by the peer to explore other Evolutionary Strategies (ES) that are more adaptive. This makes sense because relying solely on "nim_sum" might not always be the best approach, especially in different game scenarios. The peer's effort in trying different strategies and seeking adaptability is a positive aspect of their work.
One thing to note is that the code runs a bit slowly because of the many games played for each move in the "adaptive" strategy. Despite this, it's understandable considering the thorough evaluation process.
One small improvement could be adding a metric for the percentage of victories. Despite this suggestion, the code already prints the number of victories for both players after each set of 100 games, providing useful information about how well the strategies are performing.