Automatic Playtesting for Yahtzee
James R. Glenn, Rob Brunstad · 2020 IEEE Conference on Games (CoG) · 2020
Yahtzee is a dice game with elements of skill and chance. There are many numeric parameters that govern the game's scoring rules, and varying those parameters will affect many aspects of game play, including strategic depth. We take advantage of the ease of computing the optimal policy for solitaire Yahtzee to use supervised learning to develop near-optimal agents based on neural networks. With the aim of automatically selecting scoring parameters that increase strategic depth, we measure the agent's skill as we vary the resources available to the neural networks and use metrics derived from the resulting learning curve as an indicator of strategic depth.