  Virtual Reality (VR) games that feature physical activities have been shown
to increase players' motivation to do physical exercise. However, for such
exercises to have a positive healthcare effect, they have to be repeated
several times a week. To maintain player motivation over longer periods of
time, games often employ Dynamic Difficulty Adjustment (DDA) to adapt the
game's challenge according to the player's capabilities. For exercise games,
this is mostly done by tuning specific in-game parameters like the speed of
objects. In this work, we propose to use experience-driven Procedural Content
Generation for DDA in VR exercise games by procedurally generating levels that
match the player's current capabilities. Not only finetuning specific
parameters but creating completely new levels has the potential to decrease
repetition over longer time periods and allows for the simultaneous adaptation
of the cognitive and physical challenge of the exergame. As a proof-of-concept,
we implement an initial prototype in which the player must traverse a maze that
includes several exercise rooms, whereby the generation of the maze is realized
by a neural network. Passing those exercise rooms requires the player to
perform physical activities. To match the player's capabilities, we use Deep
Reinforcement Learning to adjust the structure of the maze and to decide which
exercise rooms to include in the maze. We evaluate our prototype in an
exploratory user study utilizing both biodata and subjective questionnaires.
