  Multi-robot cooperation requires agents to make decisions that are consistent
with the shared goal without disregarding action-specific preferences that
might arise from asymmetry in capabilities and individual objectives. To
accomplish this goal, we propose a method named SLiCC: Stackelberg Learning in
Cooperative Control. SLiCC models the problem as a partially observable
stochastic game composed of Stackelberg bimatrix games, and uses deep
reinforcement learning to obtain the payoff matrices associated with these
games. Appropriate cooperative actions are then selected with the derived
Stackelberg equilibria. Using a bi-robot cooperative object transportation
problem, we validate the performance of SLiCC against centralized multi-agent
Q-learning and demonstrate that SLiCC achieves better combined utility.
