Stagnant Neuron: Towards Understanding the Plasticity Loss in Multi-Agent Reinforcement Learning Value Factorization Methods
arXiv:2606.25335v1 Announce Type: new Abstract: Multi-Agent Reinforcement Learning (MARL) value factorization methods can suffer from a loss of plasticity, gradually failing to adapt when transferring to new task instances. We trace this issue to stagnant neurons, units whose gradient updates become negligibly small relative to their weights, thereby hindering learning. While existing plasticity injection methods exist, they prove ineffective for such neurons. To address this, we propose Knowledge-retentive Neuron-level PlastIcity Focusing InjEction (KNIFE), a novel method that directly target
The increasing complexity and scale of multi-agent reinforcement learning systems require robust plasticity mechanisms to ensure continuous adaptation and learning efficiency.
Addressing plasticity loss in MARL is crucial for developing reliable, continuously improving AI agents capable of operating in dynamic and uncertain environments.
The proposed KNIFE method offers a targeted solution to a known limitation in MARL value factorization, potentially enhancing the performance and applicability of AI agents.
- · AI research and development
- · Developers of multi-agent systems
- · Industries deploying AI agents (e.g., logistics, robotics)
- · Organizations relying on brittle MARL systems
- · Current methods that fail to address 'stagnant neurons'
Improved performance and adaptability of AI agents in complex, multi-agent environments.
Accelerated development and broader adoption of sophisticated autonomous AI agent systems across various sectors.
Increased competition in AI agent development leading to more resilient and intelligent autonomous systems.
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