Motivated by the problem of tracking a direction in a decentralized way, we consider the general problem of cooperative learning in multi-agent systems with time-varying connectivity and intermittent measurements. We propose a distributed learning protocol capable of learning an unknown vector μ from noisy measurements made independently by autonomous nodes. Our protocol is completely distributed and able to cope with the time-varying, unpredictable, and noisy nature of inter-agent communication, and intermittent noisy measurements of μ. Our main result bounds the learning speed of our protocol in terms of the size and combinatorial features of the (time-varying) networks connecting the nodes.
@article{arxiv.1209.2194,
title = {Cooperative learning in multi-agent systems from intermittent measurements},
author = {Naomi Ehrich Leonard and Alex Olshevsky},
journal= {arXiv preprint arXiv:1209.2194},
year = {2014}
}