Stagnant Neuron: Towards Understanding the Plasticity Loss in Multi-Agent Reinforcement Learning Value Factorization Methods

2026-06-24 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, medium

Summary

Multi-Agent Reinforcement Learning (MARL) value factorization methods often experience plasticity loss, hindering their adaptation to new task instances. This issue is attributed to "stagnant neurons," units whose gradient updates become negligibly small relative to their weights, impeding further learning. Existing plasticity injection techniques are ineffective for these specific neurons. To counter this, a new method called Knowledge-retentive Neuron-level PlastIcity Focusing InjEction (KNIFE) is proposed. KNIFE directly targets stagnant neurons by replacing each with a composite unit comprising three specialized components: a frozen knowledge neuron to preserve acquired information, a re-initialized active neuron to restore learning capacity, and a compensation neuron to ensure the combined output matches the original, thereby maintaining learned cooperation knowledge. Experiments on SMACv2, predator-prey, and matrix games demonstrate KNIFE's superior performance over other plasticity injection methods.

Key takeaway

For AI Scientists developing Multi-Agent Reinforcement Learning systems, if your value factorization methods struggle with adaptability or task transfer, you should investigate the "stagnant neuron" phenomenon. Consider implementing KNIFE's composite neuron strategy, which preserves existing knowledge while restoring learning capacity. This approach can significantly improve an agent's ability to adapt to new environments and maintain cooperative behaviors, as demonstrated on benchmarks like SMACv2, enhancing overall system robustness.

Key insights

"Stagnant neurons" cause plasticity loss in MARL value factorization, addressed by KNIFE's composite neuron replacement.

Principles

• Plasticity loss in MARL stems from "stagnant neurons".
• Preserving knowledge while restoring learning is key.
• Neuron-level intervention can restore adaptability.

Method

KNIFE replaces "stagnant neurons" with a three-component unit: a frozen knowledge neuron, a re-initialized active neuron, and a compensation neuron to maintain output.

In practice

• Apply KNIFE to MARL agents struggling with adaptation.
• Use composite neurons for targeted plasticity injection.
• Evaluate neuron-level gradient updates for stagnation.

Topics

• Multi-Agent Reinforcement Learning
• Value Factorization
• Neural Network Plasticity
• Stagnant Neurons
• Continual Learning
• SMACv2

Code references

• hyeon1996/S2Q

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.