Cooperative Control of Mobile Robots with Stackelberg Learning

Autor:	Koh, Joewie J., Ding, Guohui, Heckman, Christoffer, Chen, Lijun, Roncone, Alessandro
Rok vydání:	2020
Předmět:	Computer Science - Robotics Computer Science - Computer Science and Game Theory Computer Science - Machine Learning Computer Science - Multiagent Systems I.2.9 I.2.6 I.2.11
Zdroj:	Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020, pp. 7985-7992
Druh dokumentu:	Working Paper
DOI:	10.1109/IROS45743.2020.9341376
Popis:	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. Comment: 8 pages, 7 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2008.00679 Zobrazit plný text záznamu View this record from Arxiv