  In the vision and language navigation task, the agent may encounter ambiguous
situations that are hard to interpret by just relying on visual information and
natural language instructions. We propose an interactive learning framework to
endow the agent with the ability to ask for users' help in such situations. As
part of this framework, we investigate multiple learning approaches for the
agent with different levels of complexity. The simplest model-confusion-based
method lets the agent ask questions based on its confusion, relying on the
predefined confidence threshold of a next action prediction model. To build on
this confusion-based method, the agent is expected to demonstrate more
sophisticated reasoning such that it discovers the timing and locations to
interact with a human. We achieve this goal using reinforcement learning (RL)
with a proposed reward shaping term, which enables the agent to ask questions
only when necessary. The success rate can be boosted by at least 15% with only
one question asked on average during the navigation. Furthermore, we show that
the RL agent is capable of adjusting dynamically to noisy human responses.
Finally, we design a continual learning strategy, which can be viewed as a data
augmentation method, for the agent to improve further utilizing its interaction
history with a human. We demonstrate the proposed strategy is substantially
more realistic and data-efficient compared to previously proposed
pre-exploration techniques.
