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.
An autonomous coverage path planning algorithm for maritime search and rescue of persons- in-water based on deep reinforcement learning,
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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.