Scaling Vision-and-Language Navigation With Offline RL in VLN-ORL Study

Core Concepts

Reward-conditioning improves VLN agent performance on suboptimal datasets.

Abstract

The study introduces VLN-ORL, utilizing suboptimal offline trajectories for training VLN agents. It proposes a reward-conditioned approach to improve agent performance, demonstrating significant enhancements in complex environments. Various noise models are explored to generate suboptimal datasets for evaluation. The proposed reward token allows flexible conditioning of VLN agents during training and testing. Empirical studies show substantial performance improvements, especially in challenging scenarios like the Random dataset. Ablation studies confirm the effectiveness of the reward-conditioned model across different subsets of validation sets. K-fold validation results further validate the performance gains of the reward-conditioning approach.

Stats

Our experiments demonstrate that the reward-conditioned approach leads to significant performance improvements. The reward token relates to the presumed reward at each state, which can be either sparse or dense. The proposed reward token allows flexible conditioning of VLN agents during training and testing.

Quotes

"The proposed reward-conditioned approach leads to significant performance improvements." "The reward token allows flexible conditioning of VLN agents during training and testing."

Key Insights Distilled From

Scaling Vision-and-Language Navigation With Offline RL

by Valay Bundel... at arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18454.pdf

Deeper Inquiries

Why is the reward-conditioned model able to learn from suboptimal datasets?

The reward-conditioned model is able to learn from suboptimal datasets due to its conditioning on a reward token that provides valuable feedback to the model during training. By associating actions with rewards that indicate progress towards or away from the goal, the model learns to predict actions that align with the suboptimal behavior demonstrated in the dataset. This conditioning guides the model to minimize the disparity between predicted and actual actions, enabling it to understand the connection between actions and their associated rewards. This training process primes the model to predict actions contingent upon reward information, leading to improved performance on suboptimal datasets.

Is the proposed reward token too greedy?

The proposed reward token, which is based on the change in displacement between consecutive states, may seem greedy as it incentivizes the model to continuously move towards the goal. This can potentially lead to the model getting stuck in cases where it needs to consider the long-term impact of its actions. However, the reward token's design is intentional to guide the model towards the goal during testing by conditioning it on positive rewards. While this may seem greedy in some instances, it effectively trains the model to predict actions that drive progress towards the goal, even in suboptimal datasets.

Is it really not too greedy?

Despite the initial appearance of being greedy, the proposed reward token is not overly so. The reward token's conditioning on positive rewards during testing ensures that the model consistently generates actions that move it closer to the goal. While this may seem like a greedy approach, it is essential for the model to learn the desired behavior and achieve success in navigation tasks. Additionally, the reward token's design allows the model to adapt to different scenarios and datasets, demonstrating its flexibility and effectiveness in guiding the model towards optimal performance.