Summary of Rethinking Transformers in Solving Pomdps, by Chenhao Lu and Ruizhe Shi and Yuyao Liu and Kaizhe Hu and Simon S. Du and Huazhe Xu
Rethinking Transformers in Solving POMDPs
by Chenhao Lu, Ruizhe Shi, Yuyao Liu, Kaizhe Hu, Simon S. Du, Huazhe Xu
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper examines the limitations of Transformers in solving partially observable Markov decision processes (POMDPs), which is a crucial aspect of real-world reinforcement learning applications. The authors demonstrate that regular languages, which are challenging for Transformers to model, can be reduced to POMDPs, highlighting the difficulties in incorporating POMDP-specific biases into Transformer models. In contrast, recurrent neural networks (RNNs) naturally incorporate recurrence, making them more suitable for this task. As a result, the authors propose an alternative model, the Deep Linear Recurrent Unit (LRU), which outperforms Transformers in empirical experiments. This study casts doubt on the notion that Transformers are ideal sequence models for reinforcement learning and suggests LRU as a promising solution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine you’re trying to make decisions based on incomplete information. This is called partially observable Markov decision process, or POMDP. The problem is that popular algorithms like Transformers struggle with this type of situation. In fact, they can’t even model certain types of patterns, which makes them less effective. The authors propose a new approach called Deep Linear Recurrent Unit (LRU), which does better than the old way in some cases. This research challenges our understanding of what’s good for solving these kinds of decision-making problems. |
Keywords
» Artificial intelligence » Reinforcement learning » Transformer
