# Communication-Efficient Reinforcement Learning in Swarm Robotic Networks for Maze Exploration

arXiv: https://arxiv.org/abs/2305.17087 · HTML: https://mlsanity.com/paper/2305.17087/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 26 May 2023.

## Builds on (most cited first, 4 of 4)

- [How Can Robots Trust Each Other For Better Cooperation? A Relative Needs Entropy Based Robot-Robot Trust Assessment Model](https://mlsanity.com/paper/2105.07443/) (2021)
- [Multi-Drone 3D Trajectory Planning and Scheduling in Drone Assisted Radio Access Networks](https://mlsanity.com/paper/1906.00777/) (2019)
- [DGORL: Distributed Graph Optimization based Relative Localization of Multi-Robot Systems](https://mlsanity.com/paper/2210.01662/) (2022)
- [Over-communicate no more: Situated RL agents learn concise communication protocols](https://mlsanity.com/paper/2211.01480/) (2022)

## Abstract (arXiv)

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.