Cooperative Multi-Agent Policy Gradients with Sub-optimal Demonstration

Peixi Peng; Junliang Xing

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arXiv · 1812.01825

Cooperative Multi-Agent Policy Gradients with Sub-optimal Demonstration

Peixi Peng·Junliang Xing

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Abstract

Many reality tasks such as robot coordination can be naturally modelled as multi-agent cooperative system where the rewards are sparse. This paper focuses on learning decentralized policies for such tasks using sub-optimal demonstration. To learn the multi-agent cooperation effectively and tackle the sub-optimality of demonstration, a self-improving learning method is proposed: On the one hand, the centralized state-action values are initialized by the demonstration and updated by the learned decentralized policy to improve the sub-optimality. On the other hand, the Nash Equilibrium are found by the current state-action value and are used as a guide to learn the policy. The proposed method is evaluated on the combat RTS games which requires a high level of multi-agent cooperation. Extensive experimental results on various combat scenarios demonstrate that the proposed method can learn multi-agent cooperation effectively. It significantly outperforms many state-of-the-art demonstration based approaches.

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BibTeX RIS

Peixi Peng, Junliang Xing. 2018-12-05. Cooperative Multi-Agent Policy Gradients with Sub-optimal Demonstration. https://arxiv.org/abs/1812.01825

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