Summary of Augmenting Offline Reinforcement Learning with State-only Interactions, by Shangzhe Li and Xinhua Zhang
Augmenting Offline Reinforcement Learning with State-only Interactions
by Shangzhe Li, Xinhua Zhang
First submitted to arxiv on: 1 Feb 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 In this paper, researchers explore a novel approach for reinforcement learning when rewards are unavailable, but observations from the environment are accessible. They propose a method that leverages conditional diffusion models to generate high-return trajectories and blends them with offline data using a stitching algorithm. This augmented data can be applied to various downstream reinforcement learners, demonstrating superior performance over state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is getting smarter! Imagine playing a game where you get feedback on what’s happening, but not what you’re doing right or wrong. That’s basically the challenge this paper tackles. They develop a way to use observations from the environment to improve offline data, making it easier for machines to learn and make good decisions. |
Keywords
* Artificial intelligence * Reinforcement learning
