• Corpus ID: 237213211

Settling the Variance of Multi-Agent Policy Gradients

@article{Kuba2021SettlingTV,
  title={Settling the Variance of Multi-Agent Policy Gradients},
  author={Jakub Grudzien Kuba and Muning Wen and Yaodong Yang and Linghui Meng and Shangding Gu and Haifeng Zhang and David Henry Mguni and Jun Wang},
  journal={ArXiv},
  year={2021},
  volume={abs/2108.08612}
}
Policy gradient (PG) methods are popular reinforcement learning (RL) methods where a baseline is often applied to reduce the variance of gradient estimates. In multi-agent RL (MARL), although the PG theorem can be naturally extended, the effectiveness of multi-agent PG (MAPG) methods degrades as the variance of gradient estimates increases rapidly with the number of agents. In this paper , we offer a rigorous analysis of MAPG methods by, firstly, quantifying the contributions of the number of… 

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