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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
Summary
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.
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.
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