So I did a lot of digging to find out how to improve the model and found some interesting stuff, but the main problem was that i was telling the model that the target move was a draw (immitating the player’s move) instead of beating them -_-. Also the idea of the round robbin to find the best bot parametersalso fell out because the models just trained to draw each other, but it took forever to execute so I don’t know if I should maybe retry it. There is still the very valid concern that the best bot would be the one that’s the best against other bots and not players so. On a more positive note, I introduced momentum. It keeps a running memory of the past updates, like a rolling bolder that builds speed. This enables it to recongnize patterns much quicker by stacking the past changes together. Caught onto patterns of length 1, 2, 3 and 4 in 1, 4, 12, and 17 moves, respectively. Below is a comparison (100 games of normal human rock paper scissors, not repeating a pattern, or atleast not conciously) vs random ouput (left) and the new bot (right). Glad to see it’s doing better. Will do more research on improving but polishing the game now is a better priority.