In-Context Learning for Sequential Decision Making: A Comparative Study

In this research paper, the authors explore the effectiveness of using various transformer model configurations for offline reinforcement learning. They investigate the impact of different parameters such as the number of layers, model dimension, and attention heads on the performance of the model. The authors use a MiniHack environment to test their models and evaluate their performance in terms of average episode return.
The authors begin by stating that increasing the dataset size can improve the one-shot performance but eventually, the improvements plateau beyond a certain threshold due to lack of diversity in the training samples. They also mention that using automatic data augmentation techniques can help improve generalization in deep reinforcement learning.
The authors then present a list of transformer model configurations with varying numbers of parameters in Table 2. These configurations include details on the number of layers, model dimension, and attention heads. They also discuss how these parameters affect the performance of the model.
To illustrate the effect of dataset size on the average episode return, the authors create a figure (Figure 7) that shows the improvement in performance as the dataset size increases from 2k to 30k levels. The figure indicates that there is a significant improvement in one-shot performance when increasing the dataset size but eventually, the improvements plateau beyond a certain threshold.
The authors also discuss other related works such as Roberta et al.’s (2020) paper on automatic data augmentation for generalization in deep reinforcement learning and Machel et al.’s (2022) paper on using Wikipedia to help offline reinforcement learning.
In summary, the authors of this research paper investigate the effectiveness of various transformer model configurations for offline reinforcement learning and find that increasing the dataset size can improve performance but eventually reaches a plateau beyond a certain threshold due to lack of diversity in the training samples. They also discuss other related works on automatic data augmentation and using Wikipedia to help offline reinforcement learning.