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

• URL: http://arxiv.org/abs/2104.13446v1
• Date: Tue, 27 Apr 2021 19:37:01 GMT
• Title: Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients
• Authors: Bozhidar Vasilev, Tarun Gupta, Bei Peng, Shimon Whiteson
• Abstract summary: 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 show that our methods perform as well or better than state-of-the-art value-based methods on a variety of SMAC tasks.
• Score: 51.749831824106046
• License: http://creativecommons.org/licenses/by/4.0/
• 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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