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arXiv · 2104.13446

Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients

Bozhidar Vasilev·Tarun Gupta·Bei Peng·Shimon Whiteson
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Abstract

Policy gradient methods are an attractive approach to multi-agent reinforcement learning problems due to their convergence properties and robustness in partially observable scenarios. However, there is a significant performance gap between state-of-the-art policy gradient and value-based methods on the popular StarCraft Multi-Agent Challenge (SMAC) benchmark. In this paper, we introduce semi-on-policy (SOP) training as an effective and computationally efficient way to address the sample inefficiency of on-policy policy gradient methods. We enhance two state-of-the-art policy gradient algorithms with SOP training, demonstrating significant performance improvements. Furthermore, we show that our methods perform as well or better than state-of-the-art value-based methods on a variety of SMAC tasks.

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Bozhidar Vasilev, Tarun Gupta, Bei Peng, Shimon Whiteson. 2021-04-27. Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients. https://arxiv.org/abs/2104.13446

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