A new composite metric, Loss of Information Utility, combines delay and squared estimation error, and a semi-decentralized multi-agent reinforcement learning scheduler minimizes it for robot team D2D communications.
Deep reinforcement learning based computation offloading and trajectory planning for multi-UA V cooperative target search,
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A Novel Indicator for Quantifying and Minimizing Information Utility Loss of Robot Teams
A new composite metric, Loss of Information Utility, combines delay and squared estimation error, and a semi-decentralized multi-agent reinforcement learning scheduler minimizes it for robot team D2D communications.