The Memory Pattern Agent has scored top 5% in the Kaggle RPS Competition.

Rock, Paper, Scissors (sometimes called roshambo) has been a staple to settle playground disagreements or determine who gets to ride in the front seat on a road trip. The game is simple, with a balance of power. There are three options to choose from, each winning or losing to the other two. In a series of truly random games, each player would win, lose, and draw roughly one-third of the games. But people are not truly random, which provides a fun opportunity for AI.

Studies have shown that a Rock, Paper, Scissors AI can consistently beat human opponents. With previous games as input, it studies patterns to understand a player’s tendencies. But what happens when we expand the simple “Best-of-3” game to be “Best-of-1000”? How well can artificial intelligence perform?

In this simulation competition, you will create an AI to play against others in many rounds of this classic game. Can you find patterns to make yours win more often than it loses? It’s possible to greatly outperform a random player when the matches involve non-random agents. A strong AI can consistently beat predictable AI.

This problem is fundamental to the fields of machine learning, artificial intelligence, and data compression. There are even potential applications in human psychology and hierarchical temporal memory. Additional information about the competition can be found on the Kaggle Competition page.

Github issues and pull requests are welcome. Your feedback is much appreciated!

March 2021, Abdelghani Belgaid