  Although Reinforcement Learning (RL) has shown impressive results in games
and simulation, real-world application of RL suffers from its instability under
changing environment conditions and hyperparameters. We give a first impression
of the extent of this instability by showing that the hyperparameters found by
automatic hyperparameter optimization (HPO) methods are not only dependent on
the problem at hand, but even on how well the state describes the environment
dynamics. Specifically, we show that agents in contextual RL require different
hyperparameters if they are shown how environmental factors change. In
addition, finding adequate hyperparameter configurations is not equally easy
for both settings, further highlighting the need for research into how
hyperparameters influence learning and generalization in RL.
