Game AI of StarCraft II based on Deep Reinforcement Learning
thesisposted on 30.04.2020 by Junjie Luo
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
The research problem of this article is the Game AI agent of StarCraft II based on Deep Reinforcement Learning (DRL). StarCraft II is viewed as the most challenging Real-time Strategy (RTS) game for now, and it is also the most popular game where researchers are developing and improving AI agents. Building AI agents of StarCraft II can help researchers on machine learning figure out the weakness of DRL and improve this series of algorithms. In 2018, DeepMind and Blizzard developed the StarCraft II Learning Environment (PySC2) to enable researchers to promote the development of AI agents. DeepMind started to develop a new project called AlphaStar after AlphaGo based on DRL, while several laboratories also published articles about the AI agents of StarCraft II. Most of them are researching on the AI agents of Terran and Zerg, which are two of three races in StarCraft II. AI agents show high-level performance compared with most StarCraft II players. However, the performance is far from defeating E-sport players because Game AI for StarCraft II has large observation space and large action space. However, there is no publication on Protoss, which is the remaining and most complicated race to deal with (larger action space, larger observation space) for AI agents due to its characteristics. Thus, in this paper, the research question is whether the AI agent of Protoss, which is developed by the model based on DRL, for a full-length game on a particular map can defeat the high-level built-in cheating AI. The population of this research design is the StarCraft II AI agents that researchers built based on their DRL models, while the sample is the Protoss AI agent in this paper. The raw data is from the game matches between the Protoss AI agent and built-in AI agents. PySC2 can capture features and numerical variables in each match to obtain the training data. The expected outcome is the model based on DRL, which can train a Protoss AI agent to defeat high-level game AI agents with the win rate. The model includes the action space of Protoss, the observation space and the realization of DRL algorithms. Meanwhile, the model is built on PySC2 v2.0, which provides additional action functions. Due to the complexity and the unique characteristics of Protoss in StarCraft II, the model cannot be applied to other games or platforms. However, how the model trains a Protoss AI agent can show the limitation of DRL and push DRL algorithm a little forward.