mute-communication-unlearning-multi-agent

name	mute-communication-unlearning-multi-agent
description	Return-preserving communication unlearning for efficient multi-agent coordination under bandwidth constraints. Enables MARL agents to selectively forget inter-agent communications while preserving coordination returns, addressing the trade-off between communication sparsity and cooperative performance. Activation: MUTE, communication unlearning, MARL, bandwidth constraints, cooperative games, inter-agent communication, partial observability.
metadata	{"arxiv_id":"2607.03473","published":"2026-07-03","authors":"Rui Zuo, Qinwei Huang, Mingyang Li, Zhenhang Zhang, Simon Khan, Qinru Qiu","tags":["mute","communication-unlearning","marl","bandwidth-constraints","cooperative-games","inter-agent-communication","partial-observability"]}

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination

Overview

Inter-agent communication is critical for coordinating Multi-Agent Reinforcement Learning (MARL) agents under partial observability to perform effectively in cooperative games; however, real-world bandwidth constraints demand sparse interactions. Prior approaches primarily address this trade-off by designing communication protocols. MUTE introduces a complementary approach: selectively unlearning (forgetting) communications while preserving coordination returns.

Key Problem

Communication Efficiency in MARL

• Agents in cooperative games need to communicate to coordinate under partial observability
• Real-world bandwidth constraints require sparse communication
• Traditional approach: design more efficient communication protocols
• MUTE's approach: selectively forget communications that don't contribute to returns

Key Innovations

Communication Unlearning

• Selectively removes learned communication behaviors that are redundant or low-impact
• Preserves the cooperative return (team reward) despite reduced communication
• Complementary to existing communication-efficient MARL methods

Return-Preserving Guarantee

• Unlearning process explicitly targets communications that don't affect coordination returns
• Ensures that removing communication doesn't degrade team performance
• Distinguishes between communications that are critical vs. those that are noise

Methodology

1. Communication Analysis: Identify which inter-agent messages contribute to returns
2. Unlearning Process: Selectively remove non-critical communication behaviors from trained policies
3. Return Preservation: Validate that team performance is maintained after unlearning
4. Bandwidth Reduction: Measure communication sparsity achieved while preserving returns

Implications

• Communication unlearning as a new paradigm for MARL efficiency
• Complements existing bandwidth-efficient communication protocols
• Demonstrates that trained MARL policies often have redundant communication
• Practical for deploying MARL in bandwidth-constrained real-world environments

Pitfalls

• Unlearning may remove communications that are marginally useful in edge cases
• Return preservation is validated empirically, not with formal guarantees
• Unlearning process itself has computational cost
• May not generalize across different cooperative game structures
• Interaction with existing communication-efficient methods needs further study

Activation Keywords

MUTE, communication unlearning, MARL, bandwidth constraints, cooperative games, inter-agent communication, partial observability, communication sparsity, return preservation, multi-agent coordination

Paper Reference

arXiv:2607.03473 - "MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination" (Jul 2026)