IEEE游戏汇刊(T-G)发表关于游戏的科学、技术和工程方面的高质量原创文章。本杂志的文章按照IEEE PSPB操作手册(章节8.2.1.C和8.2.2.A)的要求进行同行评审。每一篇发表的文章都由至少两名独立的审稿人通过单盲的同行评审过程进行评审,审稿人的身份作者并不知道,但审稿人知道作者的身份。文章在被接受前筛选是否抄袭。 官网地址:http://dblp.uni-trier.de/db/journals/tciaig/

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Injecting human knowledge is an effective way to accelerate reinforcement learning (RL). However, these methods are underexplored. This paper presents our discovery that an abstract forward model (Thought-game (TG)) combined with transfer learning is an effective way. We take StarCraft II as the study environment. With the help of a designed TG, the agent can learn a 99\% win-rate on a 64$\times$64 map against the Level-7 built-in AI, using only 1.08 hours in a single commercial machine. We also show that the TG method is not as restrictive as it was thought to be. It can work with roughly designed TGs, and can also be useful when the environment changes. Comparing with previous model-based RL, we show TG is more effective. We also present a TG hypothesis that gives the influence of fidelity levels of TG. For real games that have unequal state and action spaces, we proposed a novel XfrNet of which usefulness is validated while achieving a 90\% win-rate against the cheating Level-10 AI. We argue the TG method might shed light on further studies of efficient RL with human knowledge.

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