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|Abstract:||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 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) network connecting the nodes. © 2013 IEEE.|
|Citation:||Leonard, NE, Olshevsky, A. (2013). Cooperative learning in multi-agent systems from intermittent measurements. Proceedings of the IEEE Conference on Decision and Control, 7492 - 7497. doi:10.1109/CDC.2013.6761079|
|Pages:||7492 - 7497|
|Type of Material:||Conference Proceeding|
|Journal/Proceeding Title:||Proceedings of the IEEE Conference on Decision and Control|
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