Cooperative learning in multi-agent systems from intermittent measurements

Naomi Ehich Leonard and Alex Olshevsky

Proceedings of the IEEE Conference on Decision and Control, Florence, Italy, 2013.
Motivated by the problem of decentralized direction-tracking, 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 $\mu$ 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 $\mu$. Our main result bounds the learning speed of our protocol in terms of the size and combinatorial features of the (time-varying) network connecting the nodes.

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