  State representation learning aims at learning compact representations from
raw observations in robotics and control applications. Approaches used for this
objective are auto-encoders, learning forward models, inverse dynamics or
learning using generic priors on the state characteristics. However, the
diversity in applications and methods makes the field lack standard evaluation
datasets, metrics and tasks. This paper provides a set of environments, data
generators, robotic control tasks, metrics and tools to facilitate iterative
state representation learning and evaluation in reinforcement learning
settings.
