KD-MARL: Resource-Aware Knowledge Distillation in Multi-Agent Reinforcement Learning

2026-04-10 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Expert, extended

Summary

KD-MARL is a two-stage knowledge distillation framework designed to enable multi-agent reinforcement learning (MARL) deployment in resource-constrained environments. It addresses limitations of existing methods by transferring coordinated behavior from a centralized, high-capacity expert policy to lightweight, decentralized student agents. The framework trains student policies without a critic, instead relying on distilled advantage signals and structured policy supervision to maintain coordination under heterogeneous and limited observations. KD-MARL supports heterogeneous student architectures, allowing each agent's model capacity to align with its observation complexity. Experiments on SMAC and MPE benchmarks demonstrate that KD-MARL retains over 90% of expert performance while reducing computational cost by up to 28.6x FLOPs and inference time by approximately 40%.

Key takeaway

For AI Engineers and Research Scientists developing MARL systems for embedded or edge platforms, KD-MARL offers a practical solution to overcome computational and memory constraints. By adopting its two-stage, critic-free distillation approach, you can achieve near-expert coordination performance with significantly reduced resource overhead, making real-world deployment feasible. Consider implementing heterogeneous student architectures to further optimize efficiency based on agent-specific observation complexity.

Key insights

KD-MARL distills expert coordination into lightweight, critic-free agents for efficient MARL deployment in resource-constrained settings.

Principles

• Decouple centralized learning from decentralized execution.
• Align agent model capacity with observation complexity.
• Preserve coordination through structured distillation.

Method

A two-stage process: train a centralized expert, then distill its knowledge into critic-free student agents using teacher-guided advantage distillation and a composite loss combining action-policy fidelity, structural relation, and coordinated role-based components.

In practice

• Deploy MARL on edge devices with limited compute.
• Reduce FLOPs by up to 28.6x for MARL inference.
• Maintain multi-agent coordination under partial observations.

Topics

• KD-MARL Framework
• Multi-Agent Reinforcement Learning
• Knowledge Distillation
• Resource-Constrained Deployment
• Heterogeneous Architectures

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.