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Learning to Cooperate via an Attention-Based Communication Neural Network in Decentralized Multi-Robot Exploration.

Mingyang Geng¹, Kele Xu¹, Xing Zhou¹

¹National Key Laboratory of Parallel and Distributed Processing, College of Computer, National University of Defense Technology, Changsha 410073, China.

Entropy (Basel, Switzerland)

|December 3, 2020

Summary

This study introduces an Attention-based Communication neural network (CommAttn) for decentralized multi-robot exploration. This AI approach learns cooperation strategies automatically, outperforming human-designed methods in complex environments.

Keywords:

attention mechanism deep reinforcement learning dynamic environments multi-robot exploration

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Area of Science:

Robotics
Artificial Intelligence
Multi-Agent Systems

Background:

Decentralized multi-robot exploration requires effective cooperation without a central controller.
Human-designed strategies like frontier-based methods struggle in complex, dynamic environments.
Automating cooperation strategy learning is crucial for advanced robotic applications.

Purpose of the Study:

To present a novel Attention-based Communication neural network (CommAttn) for decentralized multi-robot exploration.
To enable robots to learn cooperation strategies automatically through explicit communication.
To improve exploration efficiency by enabling selective communication based on message relevance.

Main Methods:

Developed a communication neural network enabling robots to learn cooperation strategies.
Integrated an attention mechanism to assess communication necessity between agent pairs.
Utilized a simulated multi-robot disaster exploration scenario for empirical evaluation.

Main Results:

The proposed CommAttn approach demonstrated superior performance in the exploration task.
CommAttn outperformed traditional human-designed cooperation strategies.
The learning-based method also surpassed other competing AI-driven approaches.

Conclusions:

Learned cooperation strategies via CommAttn are effective for decentralized multi-robot exploration.
The attention mechanism enhances efficiency by enabling targeted communication.
This approach offers a promising alternative to human-designed strategies in complex scenarios.