A reinforcement learning environment with adversary agents is proposed in this work for pursuit-evasion game in the presence of fog of war, which is of both scientific significance and practical importance in aerospace applications. One of the most popular learning environments, StarCraft, is adopted here and the associated mini-games are analyzed to identify the current limitation for training adversary agents. The key contribution includes the analysis of the potential performance of an agent by incorporating control and differential game theory into the specific reinforcement learning environment, and the development of an adversary agents challenge (SAAC) environment by extending the current StarCraft mini-games. The subsequent study showcases the use of this learning environment and the effectiveness of an adversary agent for evasion units. Overall, the proposed SAAC environment should benefit pursuit-evasion studies with rapidly-emerging reinforcement learning technologies. Last but not least, the corresponding tutorial code can be found at GitHub.
翻译:在这项工作中,提议在战争雾条件下,与敌对物剂建立强化学习环境,以进行追逐-逃避游戏,这既具有科学意义,又对航空航天应用具有实际重要性。这里采用最受欢迎的学习环境之一,即StarCraft, 并分析相关的微型游戏,以确定目前训练敌对物剂的限制。主要贡献包括分析一个代理人的潜在表现,将控制和差别游戏理论纳入具体的加强学习环境,并通过扩大目前的StarCraft微型游戏来发展对抗物剂(SAAC)环境。随后的研究展示了这种学习环境的使用情况和敌对物剂对躲避装置的功效。总体而言,拟议的SAAC环境应有利于追赶-逃学研究,同时采用迅速形成的强化学习技术。最后但并非最不重要的一点是,在GitHub可以找到相应的辅导代码。