  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 (TL) is an effective way. We take StarCraft II as our study
environment. With the help of a designed TG, the agent can learn a 99% win-rate
on a 64x64 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 different 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 that the TG method might shed light on further studies of
efficient RL with human knowledge.
