Title: State Representation Learning for Control: An Overview,
Abstract: Representation learning algorithms are designed to learn abstract features
that characterize data. State representation learning (SRL) focuses on a
particular kind of representation learning where learned features are in low
dimension, evolve through time, and are influenced by actions of an agent. The
representation is learned to capture the variation in the environment generated
by the agent's actions; this kind of representation is particularly suitable
for robotics and control scenarios. In particular, the low dimension
characteristic of the representation helps to overcome the curse of
dimensionality, provides easier interpretation and utilization by humans and
can help improve performance and speed in policy learning algorithms such as
reinforcement learning.
This survey aims at covering the state-of-the-art on state representation
learning in the most recent years. It reviews different SRL methods that
involve interaction with the environment, their implementations and their
applications in robotics control tasks (simulated or real). In particular, it
highlights how generic learning objectives are differently exploited in the
reviewed algorithms. Finally, it discusses evaluation methods to assess the
representation learned and summarizes current and future lines of research.