  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.
