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- W4386260462 abstract "Smooth coordination within a swarm robotic system is essential for the effective execution of collective robot missions. Having efficient communication is key to the successful coordination of swarm robots. This paper proposes a new communication-efficient decentralized cooperative reinforcement learning algorithm for coordinating swarm robots. It is made efficient by hierarchically building on the use of local information exchanges. We consider a case study application of maze solving through cooperation among a group of robots, where the time and costs are minimized while avoiding inter-robot collisions and path overlaps during exploration. With a solid theoretical basis, we extensively analyze the algorithm with realistic CORE network simulations and evaluate it against state-of-the-art solutions in terms of maze coverage percentage and efficiency under communication-degraded environments. The results demonstrate significantly higher coverage accuracy and efficiency while reducing costs and overlaps even in high packet loss and low communication range scenarios." @default.
- W4386260462 created "2023-08-30" @default.
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- W4386260462 date "2023-05-20" @default.
- W4386260462 modified "2023-10-16" @default.
- W4386260462 title "Communication-Efficient Reinforcement Learning in Swarm Robotic Networks for Maze Exploration" @default.
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- W4386260462 doi "https://doi.org/10.1109/infocomwkshps57453.2023.10226167" @default.
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