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Multi-Agent Reinforcement Learning for Sample-Efficient Deep Neural Network Mapping

2025-07-22 · Srivatsan Krishnan, Jason Jabbour, Dan Zhang, Natasha Jaques, Aleksandra Faust, Shayegan Omidshafiei, Vijay Janapa Reddi arxiv

Mapping deep neural networks (DNNs) to hardware is critical for optimizing latency, energy consumption, and resource utilization, making it a cornerstone of high-performance accelerator design. Due to the vast and complex mapping space, reinforcement learning (RL) has emerged as a promising approach-but its effectiveness is often limited by sample inefficiency. We present a decentralized multi-agent reinforcement learning (MARL) framework designed to overcome this challenge. By distributing the search across multiple agents, our framework accelerates exploration. To avoid inefficiencies from training multiple agents in parallel, we introduce an agent clustering algorithm that assigns similar mapping parameters to the same agents based on correlation analysis. This enables a decentralized, parallelized learning process that significantly improves sample efficiency. Experimental results show our MARL approach improves sample efficiency by 30-300x over standard single-agent RL, achieving up to 32.61x latency reduction and 16.45x energy-delay product (EDP) reduction under iso-sample conditions.

📄 PDF Abstract BibTeX arXiv:2507.16249

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Multi-agent Reinforcement Learning

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2021-10-12 · NeurIPS 2021 12 · Weitong Zhang, Dongruo Zhou, Quanquan Gu

We study the model-based reward-free reinforcement learning with linear function approximation for episodic Markov decision processes (MDPs). In this setting, the agent works in two phases. In the exploration phase, the …

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