  Precision landing on large and small planetary bodies is a technology of
utmost importance for future human and robotic exploration of the solar system.
In this context, the Zero-Effort-Miss/Zero-Effort-Velocity (ZEM/ZEV) feedback
guidance algorithm has been studied extensively and is still a field of active
research. The algorithm, although powerful in terms of accuracy and ease of
implementation, has some limitations. Therefore with this paper we present an
adaptive guidance algorithm based on classical ZEM/ZEV in which machine
learning is used to overcome its limitations and create a closed loop guidance
algorithm that is sufficiently lightweight to be implemented on board
spacecraft and flexible enough to be able to adapt to the given constraint
scenario. The adopted methodology is an actor-critic reinforcement learning
algorithm that learns the parameters of the above-mentioned guidance
architecture according to the given problem constraints.
