  Reading to act is a prevalent but challenging task which requires the ability
to reason from a concise instruction. However, previous works face the semantic
mismatch between the low-level actions and the high-level language descriptions
and require the human-designed curriculum to work properly. In this paper, we
present a Feudal Reinforcement Learning (FRL) model consisting of a manager
agent and a worker agent. The manager agent is a multi-hop plan generator
dealing with high-level abstract information and generating a series of
sub-goals in a backward manner. The worker agent deals with the low-level
perceptions and actions to achieve the sub-goals one by one. In comparison, our
FRL model effectively alleviate the mismatching between text-level inference
and low-level perceptions and actions; and is general to various forms of
environments, instructions and manuals; and our multi-hop plan generator can
significantly boost for challenging tasks where multi-step reasoning form the
texts is critical to resolve the instructed goals. We showcase our approach
achieves competitive performance on two challenging tasks, Read to Fight
Monsters (RTFM) and Messenger, without human-designed curriculum learning.
