Summary of Temporal Logic Specification-conditioned Decision Transformer For Offline Safe Reinforcement Learning, by Zijian Guo et al.
Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning
by Zijian Guo, Weichao Zhou, Wenchao Li
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers propose a novel framework for offline safe reinforcement learning called Specification-conditioned Decision Transformer (SDT). This approach combines the expressive power of signal temporal logic (STL) and the sequential modeling capabilities of Decision Transformer (DT) to learn policies that satisfy complex temporal rules. Unlike existing approaches based on supervised learning with conditioned policies, SDT shows better performance in learning safe and high-reward policies, as demonstrated through empirical evaluations on the DSRL benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers want to train a robot or computer program to do tasks safely without getting too good at it. They’re trying to figure out how to make this work by combining two ideas: one that helps computers understand rules about time and another that helps them learn patterns in sequences of things. They tested their idea on some problems and found it works better than other approaches. |
Keywords
* Artificial intelligence * Reinforcement learning * Supervised * Transformer
