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MAD3PG: A Framework for Multi-Agent Deep Denoising Diffusion Policy Gradient Optimization
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DOI：10.1016/j.inffus.2025.104026.png)
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Abstract
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• Proposes MAD3PG, the first framework using denoising diffusion models for multi-agent value distribution estimation, and provides corresponding theoretical analysis. • Introduces a K-repeated sampling strategy with temporal-difference targets to enable efficient training of diffusion models in online reinforcement learning. • Demonstrates superior robustness and efficiency over MADDPG-type algorithms through extensive experiments in MPE and MuJoCo environments.
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