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Related Experiment Video

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Automated Interactive Video Playback for Studies of Animal Communication

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

Robust and efficient communication in multi-agent reinforcement learning.

Zejiao Liu¹, Yi Li², Jiali Wang²

¹The School of Mathematics, East China University of Science and Technology, Shanghai 200237, China.

Chaos (Woodbury, N.Y.)

|February 18, 2026

Summary

This summary is machine-generated.

This survey explores robust communication for multi-agent reinforcement learning (MARL) under real-world constraints like delays and limited bandwidth. It highlights strategies for reliable MARL systems in autonomous driving and federated learning.

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Area of Science:

Artificial Intelligence
Robotics
Machine Learning

Background:

Multi-agent reinforcement learning (MARL) enables coordinated agent behaviors.
Existing MARL communication models often assume unrealistic conditions like instantaneous and unlimited bandwidth.

Purpose of the Study:

To systematically review advances in robust and efficient communication for MARL under realistic constraints.
To focus on practical applications and identify future research directions.

Main Methods:

Review of recent literature on MARL communication strategies.
Analysis of challenges including message perturbations, transmission delays, and limited bandwidth.
Focus on applications in cooperative autonomous driving, distributed SLAM, and federated learning.

Main Results:

Identification of key challenges in low-latency reliability, bandwidth usage, and privacy trade-offs.
Exploration of MARL communication strategies tailored for real-world deployment.
Synthesis of current research to bridge the gap between theory and practice.

Conclusions:

There is a need for co-designing communication, learning, and robustness in MARL.
Future research should focus on unified approaches for practical MARL implementations.
Addressing realistic communication constraints is crucial for advancing MARL applications.