Summary of Predictive Coding For Decision Transformer, by Tung M. Luu et al.
Predictive Coding for Decision Transformer
by Tung M. Luu, Donghoon Lee, Chang D. Yoo
First submitted to arxiv on: 4 Oct 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 The paper proposes a new architecture for decision transformers (DTs) in offline reinforcement learning (RL), called Predictive Coding for Decision Transformer (PCDT). DTs have shown promise across various domains, but underperform on challenging datasets in goal-conditioned RL. This is due to the inefficiency of return conditioning for guiding policy learning, particularly in unstructured and suboptimal datasets. PCDT leverages generalized future conditioning to enhance DT methods, using an architecture that extends the DT framework conditioned on predictive codings. This enables decision-making based on both past and future factors, improving generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to improve decision transformers in offline reinforcement learning. Decision transformers are good at making decisions, but they can struggle with certain types of problems. The new approach, called PCDT, tries to help them do better by using information from the future as well as the past. This makes the decisions more general and able to work well on different kinds of tasks. |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning » Transformer
