  Increasing interest in integrating advanced robotics within manufacturing has
spurred a renewed concentration in developing real-time scheduling solutions to
coordinate human-robot collaboration in this environment. Traditionally, the
problem of scheduling agents to complete tasks with temporal and spatial
constraints has been approached either with exact algorithms, which are
computationally intractable for large-scale, dynamic coordination, or
approximate methods that require domain experts to craft heuristics for each
application. We seek to overcome the limitations of these conventional methods
by developing a novel graph attention network formulation to automatically
learn features of scheduling problems to allow their deployment. To learn
effective policies for combinatorial optimization problems via machine
learning, we combine imitation learning on smaller problems with deep
Q-learning on larger problems, in a non-parametric framework, to allow for
fast, near-optimal scheduling of robot teams. We show that our network-based
policy finds at least twice as many solutions over prior state-of-the-art
methods in all testing scenarios.
