Cooperative Control of Mobile Robots with Stackelberg Learning

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