论文标题
与Stackelberg学习对移动机器人的合作控制
Cooperative Control of Mobile Robots with Stackelberg Learning
论文作者
论文摘要
多机器人合作要求代理商做出与共同目标一致的决策，而无需忽略动作特定的偏好，而动作特定的偏好可能是由于能力和个人目标的不对称性而产生的。为了实现这一目标，我们提出了一种名为SLICC的方法：合作控制中的Stackelberg学习。 SLICC将问题建模为由Stackelberg Bimatrix游戏组成的部分可观察到的随机游戏，并使用深度强化学习来获得与这些游戏相关的回报矩阵。然后选择适当的合作行动，以衍生的Stackelberg Equilibria。使用双机器人合作对象运输问题，我们验证了SLICC对集中式多代理Q学习的性能，并证明SLICC可以实现更好的组合效用。
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.
