  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.
