  Recent research on deep learning, a set of machine learning techniques able
to learn deep architectures, has shown how robotic perception and action
greatly benefits from these techniques. In terms of spacecraft navigation and
control system, this suggests that deep architectures may be considered now to
drive all or part of the on-board decision making system. In this paper this
claim is investigated in more detail training deep artificial neural networks
to represent the optimal control action during a pinpoint landing, assuming
perfect state information. It is found to be possible to train deep networks
for this purpose and that the resulting landings, driven by the trained
networks, are close to simulated optimal ones. These results allow for the
design of an on-board real time optimal control system able to cope with large
sets of possible initial states while still producing an optimal response.
