Self-Driving Telescopes: Autonomous Scheduling of Astronomical
  Observation Campaigns with Offline Reinforcement Learning
Franco Terranova M. Voetberg Brian Nord Amanda Pagul
Modern astronomical experiments are designed to achieve multiple scientific
goals, from studies of galaxy evolution to cosmic acceleration. These goals
require data of many different classes of night-sky objects, each of which has
a particular set of observational needs. These observational needs are
typically in strong competition with one another. This poses a challenging
multi-objective optimization problem that remains unsolved. The effectiveness
of Reinforcement Learning (RL) as a valuable paradigm for training autonomous
systems has been well-demonstrated, and it may provide the basis for
self-driving telescopes capable of optimizing the scheduling for astronomy
campaigns. Simulated datasets containing examples of interactions between a
telescope and a discrete set of sky locations on the celestial sphere can be
used to train an RL model to sequentially gather data from these several
locations to maximize a cumulative reward as a measure of the quality of the
data gathered. We use simulated data to test and compare multiple
implementations of a Deep Q-Network (DQN) for the task of optimizing the
schedule of observations from the Stone Edge Observatory (SEO). We combine
multiple improvements on the DQN and adjustments to the dataset, showing that
DQNs can achieve an average reward of 87%+-6% of the maximum achievable reward
in each state on the test set. This is the first comparison of offline RL
algorithms for a particular astronomical challenge and the first open-source
framework for performing such a comparison and assessment task.