Summary of Qt-tdm: Planning with Transformer Dynamics Model and Autoregressive Q-learning, by Mostafa Kotb et al.
QT-TDM: Planning With Transformer Dynamics Model and Autoregressive Q-Learning
by Mostafa Kotb, Cornelius Weber, Muhammad Burhan Hafez, Stefan Wermter
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper explores the application of Transformers, a successful architecture in natural language processing and computer vision, to Reinforcement Learning (RL). The authors propose Transformer Dynamics Models (TDMs) for modeling environment dynamics, specifically in continuous control scenarios using Model Predictive Control (MPC). While Transformers excel in long-horizon prediction, their limitations in tokenization and autoregressive nature lead to costly planning over long horizons. To address this issue, the paper proposes a novel method, QT-TDM, that combines the robust predictive capabilities of TDMs with an autoregressive discrete Q-function using a separate Q-Transformer (QT) model for estimating long-term returns beyond short-horizon planning. The authors demonstrate the superiority of QT-TDM in performance and sample efficiency compared to existing Transformer-based RL models, achieving fast and computationally efficient inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how a successful technology called Transformers can be used in a different area called Reinforcement Learning (RL). RL helps machines learn from experience. The authors created a new way to use Transformers for modeling the environment’s behavior. They tested this method in various tasks and found it was more effective and efficient than previous methods using similar technologies. |
Keywords
» Artificial intelligence » Autoregressive » Inference » Natural language processing » Reinforcement learning » Tokenization » Transformer
