Estimating player completion rate in mobile puzzle games using
  reinforcement learning
Jeppe Theiss Kristensen Arturo Valdivia Paolo Burelli
In this work we investigate whether it is plausible to use the performance of
a reinforcement learning (RL) agent to estimate the difficulty measured as the
player completion rate of different levels in the mobile puzzle game Lily's
Garden.For this purpose we train an RL agent and measure the number of moves
required to complete a level. This is then compared to the level completion
rate of a large sample of real players.We find that the strongest predictor of
player completion rate for a level is the number of moves taken to complete a
level of the ~5% best runs of the agent on a given level. A very interesting
observation is that, while in absolute terms, the agent is unable to reach
human-level performance across all levels, the differences in terms of
behaviour between levels are highly correlated to the differences in human
behaviour. Thus, despite performing sub-par, it is still possible to use the
performance of the agent to estimate, and perhaps further model, player
metrics.