StarCraft (SC) is one of the most popular and successful Real Time Strategy
(RTS) games. In recent years, SC is also considered as a testbed for AI
research, due to its enormous state space, hidden information, multi-agent
collaboration and so on. Thanks to the annual AIIDE and CIG competitions, a
growing number of bots are proposed and being continuously improved. However, a
big gap still remains between the top bot and the professional human players.
One vital reason is that current bots mainly rely on predefined rules to
perform macro actions. These rules are not scalable and efficient enough to
cope with the large but partially observed macro state space in SC. In this
paper, we propose a DRL based framework to do macro action selection. Our
framework combines the reinforcement learning approach Ape-X DQN with
Long-Short-Term-Memory (LSTM) to improve the macro action selection in bot. We
evaluate our bot, named as LastOrder, on the AIIDE 2017 StarCraft AI
competition bots set. Our bot achieves overall 83% win-rate, outperforming 26
bots in total 28 entrants