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Largest coverage network in a robot swarm using reinforcement learning

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Abstract

Establishing a large adaptive connected network for decentralized swarms is useful for their behavior to share information about the working environment. A hard-coded implementation is time-consuming to achieve. Therefore, we are motivated to explore the benefits of reinforcement learning (RL) to learn a suitable adaptive policy. We also explore the combined use of a scalar field, which was inspired by template pheromones in social insects. In this paper, we investigate using RL with low and high-resolution scalar fields to solve the largest covering network problem. Our results show that RL outperforms the hard-coded approach in the presence of the high-resolution scalar field.

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Notes

  1. https://github.com/davechurchill/cwaggle

  2. https://github.com/BOTSlab/waggle

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Authors and Affiliations

  1. Department of Computer Science, Memorial University of Newfoundland, St. John’s, NL, Canada

    Dalia S. Ibrahim

  2. Department of Computer Systems, Faculty of Computer and Information Science, Ain-Shams University, Cairo, Egypt

    Dalia S. Ibrahim

  3. Department of Computer Science, Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL, Canada

    Andrew Vardy

Authors
  1. Dalia S. Ibrahim
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  2. Andrew Vardy
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Correspondence to Dalia S. Ibrahim.

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This work was presented in part at the joint symposium of the 27th International Symposium on Artificial Life and Robotics, the 7th International Symposium on BioComplexity, and the 5th International Symposium on Swarm Behavior and Bio-Inspired Robotics (Online, January 25-27, 2022).

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Ibrahim, D.S., Vardy, A. Largest coverage network in a robot swarm using reinforcement learning. Artif Life Robotics 27, 652–662 (2022). https://doi.org/10.1007/s10015-022-00804-4

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  • DOI: https://doi.org/10.1007/s10015-022-00804-4

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