  Robots could learn their own state and world representation from perception
and experience without supervision. This desirable goal is the main focus of
our field of interest, state representation learning (SRL). Indeed, a compact
representation of such a state is beneficial to help robots grasp onto their
environment for interacting. The properties of this representation have a
strong impact on the adaptive capability of the agent. In this article we
present an approach based on imitation learning. The idea is to train several
policies that share the same representation to reproduce various
demonstrations. To do so, we use a multi-head neural network with a shared
state representation feeding a task-specific agent. If the demonstrations are
diverse, the trained representation will eventually contain the information
necessary for all tasks, while discarding irrelevant information. As such, it
will potentially become a compact state representation useful for new tasks. We
call this approach SRLfD (State Representation Learning from Demonstration).
Our experiments confirm that when a controller takes SRLfD-based
representations as input, it can achieve better performance than with other
representation strategies and promote more efficient reinforcement learning
(RL) than with an end-to-end RL strategy.
