  Data-efficient learning in continuous state-action spaces using very
high-dimensional observations remains a key challenge in developing fully
autonomous systems. In this paper, we consider one instance of this challenge,
the pixels to torques problem, where an agent must learn a closed-loop control
policy from pixel information only. We introduce a data-efficient, model-based
reinforcement learning algorithm that learns such a closed-loop policy directly
from pixel information. The key ingredient is a deep dynamical model that uses
deep auto-encoders to learn a low-dimensional embedding of images jointly with
a predictive model in this low-dimensional feature space. Joint learning
ensures that not only static but also dynamic properties of the data are
accounted for. This is crucial for long-term predictions, which lie at the core
of the adaptive model predictive control strategy that we use for closed-loop
control. Compared to state-of-the-art reinforcement learning methods for
continuous states and actions, our approach learns quickly, scales to
high-dimensional state spaces and is an important step toward fully autonomous
learning from pixels to torques.
