Implicit Repair with Reinforcement Learning in Emergent Communication

2025-02-18

AI Summary

This work investigates implicitly emergent error-correction mechanisms in multi-agent communication under noisy channel conditions. Addressing message corruption and misinformation arising from channel and input noise in bursty communication, we extend the Lewis signaling game framework with explicit noise modeling and integrate reinforcement learning–based multi-agent training to induce autonomous evolution of redundant encoding strategies. Our contributions are threefold: (i) we provide the first systematic characterization of how implicit repair enhances communication robustness; (ii) we propose a general-purpose protocol that jointly optimizes for noise robustness and clean-channel performance, thereby improving cross-environment generalization; and (iii) empirical results demonstrate substantial gains in task success rate under high noise—matching the performance of optimal deterministic-baseline models in noise-free settings—while maintaining strong generalization across diverse noise regimes.

Technology Category

• Multiagent Systems: Agent Communication
• Search and Optimization: Learning to Search
• Natural Language Processing: Safety and Robustness

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Conversational repair is a mechanism used to detect and resolve miscommunication and misinformation problems when two or more agents interact. One particular and underexplored form of repair in emergent communication is the implicit repair mechanism, where the interlocutor purposely conveys the desired information in such a way as to prevent misinformation from any other interlocutor. This work explores how redundancy can modify the emergent communication protocol to continue conveying the necessary information to complete the underlying task, even with additional external environmental pressures such as noise. We focus on extending the signaling game, called the Lewis Game, by adding noise in the communication channel and inputs received by the agents. Our analysis shows that agents add redundancy to the transmitted messages as an outcome to prevent the negative impact of noise on the task success. Additionally, we observe that the emerging communication protocol's generalization capabilities remain equivalent to architectures employed in simpler games that are entirely deterministic. Additionally, our method is the only one suitable for producing robust communication protocols that can handle cases with and without noise while maintaining increased generalization performance levels.

Problem

• Implicit repair in emergent communication
• Redundancy to counteract noise effects
• Robust communication protocols generalization

Innovation

• Implicit repair with reinforcement learning
• Redundancy in communication protocols
• Generalization in noisy environments