  Vision-and-Language Navigation (VLN) requires an agent to find a specified
spot in an unseen environment by following natural language instructions.
Dominant methods based on supervised learning clone expert's behaviours and
thus perform better on seen environments, while showing restricted performance
on unseen ones. Reinforcement Learning (RL) based models show better
generalisation ability but have issues as well, requiring large amount of
manual reward engineering is one of which. In this paper, we introduce a Soft
Expert Reward Learning (SERL) model to overcome the reward engineering
designing and generalisation problems of the VLN task. Our proposed method
consists of two complementary components: Soft Expert Distillation (SED) module
encourages agents to behave like an expert as much as possible, but in a soft
fashion; Self Perceiving (SP) module targets at pushing the agent towards the
final destination as fast as possible. Empirically, we evaluate our model on
the VLN seen, unseen and test splits and the model outperforms the
state-of-the-art methods on most of the evaluation metrics.
