  With three complexes spread evenly across the Earth, NASA's Deep Space
Network (DSN) is the primary means of communications as well as a significant
scientific instrument for dozens of active missions around the world. A rapidly
rising number of spacecraft and increasingly complex scientific instruments
with higher bandwidth requirements have resulted in demand that exceeds the
network's capacity across its 12 antennae. The existing DSN scheduling process
operates on a rolling weekly basis and is time-consuming; for a given week,
generation of the final baseline schedule of spacecraft tracking passes takes
roughly 5 months from the initial requirements submission deadline, with
several weeks of peer-to-peer negotiations in between. This paper proposes a
deep reinforcement learning (RL) approach to generate candidate DSN schedules
from mission requests and spacecraft ephemeris data with demonstrated
capability to address real-world operational constraints. A deep RL agent is
developed that takes mission requests for a given week as input, and interacts
with a DSN scheduling environment to allocate tracks such that its reward
signal is maximized. A comparison is made between an agent trained using
Proximal Policy Optimization and its random, untrained counterpart. The results
represent a proof-of-concept that, given a well-shaped reward signal, a deep RL
agent can learn the complex heuristics used by experts to schedule the DSN. A
trained agent can potentially be used to generate candidate schedules to
bootstrap the scheduling process and thus reduce the turnaround cycle for DSN
scheduling.
