  A major challenge in modern reinforcement learning (RL) is efficient control
of dynamical systems from high-dimensional sensory observations. Learning
controllable embedding (LCE) is a promising approach that addresses this
challenge by embedding the observations into a lower-dimensional latent space,
estimating the latent dynamics, and utilizing it to perform control in the
latent space. Two important questions in this area are how to learn a
representation that is amenable to the control problem at hand, and how to
achieve an end-to-end framework for representation learning and control. In
this paper, we take a few steps towards addressing these questions. We first
formulate a LCE model to learn representations that are suitable to be used by
a policy iteration style algorithm in the latent space. We call this model
control-aware representation learning (CARL). We derive a loss function for
CARL that has close connection to the prediction, consistency, and curvature
(PCC) principle for representation learning. We derive three implementations of
CARL. In the offline implementation, we replace the locally-linear control
algorithm (e.g.,~iLQR) used by the existing LCE methods with a RL algorithm,
namely model-based soft actor-critic, and show that it results in significant
improvement. In online CARL, we interleave representation learning and control,
and demonstrate further gain in performance. Finally, we propose value-guided
CARL, a variation in which we optimize a weighted version of the CARL loss
function, where the weights depend on the TD-error of the current policy. We
evaluate the proposed algorithms by extensive experiments on benchmark tasks
and compare them with several LCE baselines.
