Cooperative Control of Mobile Robots with Stackelberg Learning
Joewie J. Koh,
Guohui Ding,
Christoffer Heckman
et al.
Abstract: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… Show more
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