A MARL framework lets robot teams optimize monitoring accuracy for dynamic indoor human activities via decentralized policies that handle variable human counts and time dependencies, outperforming coverage baselines in simulations.
Multi-robot active in- formation gathering with periodic communication,
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Cooperative Informative Sensing for Monitoring Dynamic Indoor Environments via Multi-Agent Reinforcement Learning
A MARL framework lets robot teams optimize monitoring accuracy for dynamic indoor human activities via decentralized policies that handle variable human counts and time dependencies, outperforming coverage baselines in simulations.