  Vision-and-language navigation (VLN) is a task in which an agent is embodied
in a realistic 3D environment and follows an instruction to reach the goal
node. While most of the previous studies have built and investigated a
discriminative approach, we notice that there are in fact two possible
approaches to building such a VLN agent: discriminative \textit{and}
generative. In this paper, we design and investigate a generative
language-grounded policy which uses a language model to compute the
distribution over all possible instructions i.e. all possible sequences of
vocabulary tokens given action and the transition history. In experiments, we
show that the proposed generative approach outperforms the discriminative
approach in the Room-2-Room (R2R) and Room-4-Room (R4R) datasets, especially in
the unseen environments. We further show that the combination of the generative
and discriminative policies achieves close to the state-of-the art results in
the R2R dataset, demonstrating that the generative and discriminative policies
capture the different aspects of VLN.
