How these agents learned Yahtzee

Loowit Play builds games that learn. Both opponents here are neural networks trained from scratch with reinforcement learning (PPO) — they started knowing nothing and played millions of games, improving by trial and error until strategy rose above the noise.

Curriculum, not chaos

Rather than dropping the agent into full games, training used a curriculum: first master single categories, then the upper section, then the lower section, then the full game. Skills build up layer by layer — you can't play a good full game without knowing how to optimise a single category first.

Two different goals

"Shared dice" — removing luck

When the agents are compared, they draw from the same dice stream, so any difference in score comes from strategy, not lucky rolls. The Winner was trained this way against a league of opponents, learning when to play safe and when to gamble.

The hint button

The "What would the agents do?" button runs each agent's policy on your exact dice and scorecard and shows its recommended move. Watch for spots where Max-Score and Winner disagree — that's the Winner taking a risk to win, even if it lowers its expected score.