Summary of Rrls : Robust Reinforcement Learning Suite, by Adil Zouitine et al.
RRLS : Robust Reinforcement Learning Suite
by Adil Zouitine, David Bertoin, Pierre Clavier, Matthieu Geist, Emmanuel Rachelson
First submitted to arxiv on: 12 Jun 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 This paper addresses the crucial problem of robust reinforcement learning, focusing on developing optimal control policies that can perform well in uncertain real-world environments. The authors introduce the Robust Reinforcement Learning Suite (RRLS), a benchmark suite for evaluating algorithms against diverse adversarial scenarios. RRLS comprises six continuous control tasks with two types of uncertainty sets, enabling reproducible and comparable experiments. The paper aims to standardize robust reinforcement learning benchmarks, facilitating state-of-the-art contributions and expanding the scope to new environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make computers learn better in unexpected situations by creating a set of challenges for testing how well algorithms perform in uncertain environments. The authors made a special tool called RRLS that gives six different tasks and two kinds of uncertainty to help test algorithms. This makes it easier for researchers to compare their results and develop new ways to make algorithms more robust. |
Keywords
» Artificial intelligence » Reinforcement learning
