Summary of Debiased Offline Representation Learning For Fast Online Adaptation in Non-stationary Dynamics, by Xinyu Zhang et al.
Debiased Offline Representation Learning for Fast Online Adaptation in Non-stationary Dynamics
by Xinyu Zhang, Wenjie Qiu, Yi-Chen Li, Lei Yuan, Chengxing Jia, Zongzhang Zhang, Yang Yu
First submitted to arxiv on: 17 Feb 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 In this paper, researchers tackle the challenge of developing policies for real-world reinforcement learning applications that can adapt to non-stationary environments. They propose a novel approach called Debiased Offline Representation for fast online Adaptation (DORA) that leverages an information bottleneck principle to differentiate between changes in environment dynamics and shifts in behavior policy. DORA is designed to maximize mutual information between dynamics encoding and environmental data while minimizing mutual information between dynamics encoding and actions of the behavior policy. The authors demonstrate a practical implementation of DORA, which outperforms existing baselines across six benchmark MuJoCo tasks with variable parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to help machines learn better in changing situations. Right now, it’s hard for machines to tell when the world around them is changing and when their own actions are causing changes. To solve this problem, scientists created a new way called DORA (Debiased Offline Representation for fast online Adaptation). DORA helps machines understand the world better by using a special trick that separates changes in the environment from changes in what the machine is doing. This new approach works really well and can be used to make machines smarter. |
Keywords
* Artificial intelligence * Reinforcement learning
