In the Vision-and-Language Navigation (VLN) field, agents are tasked with
navigating real-world scenes guided by linguistic instructions. Enabling the
agent to adhere to instructions throughout the process of navigation represents
a significant challenge within the domain of VLN. To address this challenge,
common approaches often rely on encoders to explicitly record past locations
and actions, increasing model complexity and resource consumption. Our
proposal, the Vision-and-Language Navigation Generative Pretrained Transformer
(VLN-GPT), adopts a transformer decoder model (GPT2) to model trajectory
sequence dependencies, bypassing the need for historical encoding modules. This
method allows for direct historical information access through trajectory
sequence, enhancing efficiency. Furthermore, our model separates the training
process into offline pre-training with imitation learning and online
fine-tuning with reinforcement learning. This distinction allows for more
focused training objectives and improved performance. Performance assessments
on the VLN dataset reveal that VLN-GPT surpasses complex state-of-the-art
encoder-based models.

在 Vision-and-Language Navigation (VLN) 领域中，我们提出了一种基于 Transformer 的生成预训练模型（VLN-GPT），通过建模轨迹序列依赖关系来增强效率，并在训练过程中采用离线预训练和在线优化的方法，性能评估表明 VLN-GPT 超越了复杂的基于编码器的模型。