  Not having access to compact and meaningful representations is known to
significantly increase the complexity of reinforcement learning (RL). For this
reason, it can be useful to perform state representation learning (SRL) before
tackling RL tasks. However, obtaining a good state representation can only be
done if a large diversity of transitions is observed, which can require a
difficult exploration, especially if the environment is initially reward-free.
To solve the problems of exploration and SRL in parallel, we propose a new
approach called XSRL (eXploratory State Representation Learning). On one hand,
it jointly learns compact state representations and a state transition
estimator which is used to remove unexploitable information from the
representations. On the other hand, it continuously trains an inverse model,
and adds to the prediction error of this model a $k$-step learning progress
bonus to form the maximization objective of a discovery policy. This results in
a policy that seeks complex transitions from which the trained models can
effectively learn. Our experimental results show that the approach leads to
efficient exploration in challenging environments with image observations, and
to state representations that significantly accelerate learning in RL tasks.
