A Curriculum Learning Based Multi-agent Reinforcement Learning Method for Realtime Strategy Game
Dayu Zhang, Weidong Bao, Wenqian Liang, Guanlin Wu, Jiang Cao · 2022
Real-time strategy games are one of the important scenarios for studying multi-agent reinforcement learning, and there have been some researchers who have achieved some results in the field. However, limited by problems such as the complexity of the environment, these methods not only take up a large number of computational resources but also require a long training time. We based on the idea of curriculum Learning, the training process of real-time strategy game models is turned into an incremental level training. It can reduce the time cost on model training and the amount of resources required. In this study, we use StarCraft 2 as a simulation environment for real-time strategy games, and use PPO as the base algorithm to design a reinforcement learning model training method that incorporates the idea of curriculum Learning. We hope that this study is a guide to improve the efficiency of multi-agent reinforcement learning.