Summary of Efficient Duple Perturbation Robustness in Low-rank Mdps, by Yang Hu et al.
Efficient Duple Perturbation Robustness in Low-rank MDPs
by Yang Hu, Haitong Ma, Bo Dai, Na Li
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 authors of this paper introduce a novel approach to reinforcement learning (RL) that addresses the issue of efficiency in existing robust methods. They propose “duple perturbation robustness,” which involves perturbing both feature and factor vectors for low-rank Markov decision processes (MDPs). This approach is compatible with function representation views, making it applicable to practical RL problems with large or continuous state-action spaces. The authors also develop a provably efficient and practical algorithm with theoretical convergence rate guarantees. Examples are provided to illustrate the new robustness concept, and its efficiency is supported by both theoretical bounds and numerical simulations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make reinforcement learning more reliable and usable in real-world situations. The researchers have come up with a new way to make sure that machines learning from experience don’t get tricked into making bad decisions because of mistakes or unusual data. They call this “duple perturbation robustness,” which means they add noise to two kinds of information: the features that describe what’s happening and the factors that influence the decisions. This makes their approach more practical for big problems where there are many possibilities. The authors also developed a new algorithm that is efficient and works well in practice. |
Keywords
* Artificial intelligence * Reinforcement learning
