Finding the optimal human strategy for Wordle using maximum correct letter probabilities and reinforcement learning
This addresses the challenge for human Wordle players who cannot use complex algorithms, though it is incremental as it builds on existing strategies.
The paper tackled the problem of finding an optimal human strategy for Wordle by developing methods for starting words and a reinforcement learning framework, resulting in rules that human players can use to optimize their winning chance.
Wordle is an online word puzzle game that gained viral popularity in January 2022. The goal is to guess a hidden five letter word. After each guess, the player gains information about whether the letters they guessed are present in the word, and whether they are in the correct position. Numerous blogs have suggested guessing strategies and starting word lists that improve the chance of winning. Optimized algorithms can win 100% of games within five of the six allowed trials. However, it is infeasible for human players to use these algorithms due to an inability to perfectly recall all known 5-letter words and perform complex calculations that optimize information gain. Here, we present two different methods for choosing starting words along with a framework for discovering the optimal human strategy based on reinforcement learning. Human Wordle players can use the rules we discover to optimize their chance of winning.