  Modern astronomical surveys have multiple competing scientific goals.
Optimizing the observation schedule for these goals presents significant
computational and theoretical challenges, and state-of-the-art methods rely on
expensive human inspection of simulated telescope schedules. Automated methods,
such as reinforcement learning, have recently been explored to accelerate
scheduling. However, there do not yet exist benchmark data sets or
user-friendly software frameworks for testing and comparing these methods. We
present DeepSurveySim -- a high-fidelity and flexible simulation tool for use
in telescope scheduling. DeepSurveySim provides methods for tracking and
approximating sky conditions for a set of observations from a user-supplied
telescope configuration. We envision this tool being used to produce benchmark
data sets and for evaluating the efficacy of ground-based telescope scheduling
algorithms, particularly for machine learning algorithms that would suffer in
efficacy if limited to real data for training.We introduce three example survey
configurations and related code implementations as benchmark problems that can
be simulated with DeepSurveySim.
