スタッケルベルク学習による移動ロボットの協調制御
Cooperative Control of Mobile Robots with Stackelberg Learning
能力差や個別目的を持つ複数ロボットの協調のため、部分観測確率ゲームをスタッケルベルク二行列ゲームでモデル化し、深層強化学習で利得行列を学習して均衡に基づく協調行動を選ぶ手法SLiCCを提案し、2台協調搬送で有効性を示した。
著者: Joewie J. Koh, Guohui Ding, Christoffer Heckman, Lijun Chen, Alessandro Roncone
分類: cs.RO, cs.GT, cs.LG, cs.MA
原文アブストラクト
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.
