Cooperative Control of Mobile Robots with Stackelberg Learning
This addresses coordination challenges in multi-robot systems with asymmetric capabilities, though it appears incremental as it builds on existing game theory and RL methods.
The paper tackles multi-robot cooperation by proposing SLiCC, which models the problem as Stackelberg games and uses deep reinforcement learning to derive payoff matrices, achieving better combined utility than centralized multi-agent Q-learning in a bi-robot object transportation task.
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.