Steven Daniluk and M. Reza Emami
Robot teams, robot learning, multi-robot coordination, advice mechanism
This paper presents an advice mechanism compatible with heterogeneous advisers that incorporates advice into the advisee’s policy via a method guaranteeing convergence to an optimal policy. Further, the mechanism has the capability to use multiple advisers at each time step and decide when advice should be requested and accepted, such that the use of advice decreases over time. Experiments are formed with a simulated team of heterogeneous robots performing a foraging task. We show that the proposed mechanism can provide a performance improvement for homogeneous and heterogeneous robot teams, and the use of advice decreases over time.
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