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Learning Attentional Communication with a Common Network for Multiagent Reinforcement Learning.

Computational intelligence and neuroscienceยท2023
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Related Experiment Video

Updated: Sep 24, 2025

P50 Sensory Gating in Infants
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Learning Attentional and Gated Communication via Curiosity.

Chuxiong Sun1,2, Kaijie Zhou2, Cong Cong1,2

  • 1University of Chinese Academy of Sciences, Huairou, Beijing 101400, China.

Computational Intelligence and Neuroscience
|May 6, 2022
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Summary
This summary is machine-generated.

This study introduces intrinsic motivation to multi-agent communication, using curiosity to guide when and whom agents communicate with. This approach enhances cooperation in decentralized systems, especially with sparse rewards.

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Area of Science:

  • Artificial Intelligence
  • Multi-Agent Systems
  • Robotics

Background:

  • Decentralized multi-agent systems require effective communication for cooperation.
  • Current methods often rely on extrinsic rewards, limiting performance with sparse or noisy environmental feedback.
  • Training communication and policies together using extrinsic rewards presents a credit assignment challenge.

Purpose of the Study:

  • To enhance multi-agent communication efficiency by incorporating intrinsic motivation.
  • To address limitations of extrinsic reward-driven communication in partially observable environments.
  • To develop a method for agents to learn when and whom to communicate with autonomously.

Main Methods:

  • Introduced intrinsic motivation, specifically curiosity, to model communication importance.
  • Developed a heuristic mechanism at the sending end to prune unnecessary messages (gating).
  • Utilized intrinsic importance at the receiving end to differentiate and prioritize information.

Main Results:

  • Curiosity effectively models communication importance in multi-agent scenarios.
  • Gated communication significantly improves efficiency and reduces communication costs.
  • The proposed method complements existing extrinsic reward-driven approaches.

Conclusions:

  • Intrinsic motivation, particularly curiosity, is a valuable mechanism for improving multi-agent communication.
  • Learning to gate messages based on intrinsic importance enhances cooperative behavior and efficiency.
  • The method shows promise for real-world applications requiring efficient communication in decentralized systems.