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Starcraft II (SC2) is widely considered as the most challenging Real TimeStrategy (RTS) game. The underlying challenges include a large observationspace, a huge (continuous and infinite) action space, partial observations,simultaneous move for all players, and long horizon delayed rewards for localdecisions. To push the frontier of AI research, Deepmind and Blizzard jointlydeveloped the StarCraft II Learning Environment (SC2LE) as a testbench ofcomplex decision making systems. SC2LE provides a few mini games such asMoveToBeacon, CollectMineralShards, and DefeatRoaches, where some AI agentshave achieved the performance level of human professional players. However, forfull games, the current AI agents are still far from achieving humanprofessional level performance. To bridge this gap, we present two full game AIagents in this paper - the AI agent TStarBot1 is based on deep reinforcementlearning over a flat action structure, and the AI agent TStarBot2 is based onhard-coded rules over a hierarchical action structure. Both TStarBot1 andTStarBot2 are able to defeat the built-in AI agents from level 1 to level 10 ina full game (1v1 Zerg-vs-Zerg game on the AbyssalReef map), noting that level8, level 9, and level 10 are cheating agents with unfair advantages such asfull vision on the whole map and resource harvest boosting. To the best of ourknowledge, this is the first public work to investigate AI agents that candefeat the built-in AI in the StarCraft II full game.

TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game