Summary of Solving Robust Mdps As a Sequence Of Static Rl Problems, by Adil Zouitine et al.
Solving robust MDPs as a sequence of static RL problems
by Adil Zouitine, Matthieu Geist, Emmanuel Rachelson
First submitted to arxiv on: 8 Oct 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 Reinforcement learning (RL) has long sought to design control policies that maintain performance above a given threshold across various environments. This paper tackles the notoriously difficult problem of static transition model uncertainty, where environment dynamics remain unchanged throughout interaction episodes. Building upon the dynamic model’s limitations, this work proposes a novel approach to solve robust MDPs by solving a series of static problems. The IWOCS meta-algorithm incrementally identifies worst-case transition models, guiding policy optimization and decoupling it from adversarial transition functions. This paper also derives a deep RL version of IWOCS, demonstrating its competitiveness with state-of-the-art algorithms on classical benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research makes a big step forward in developing artificial intelligence that can work well in different situations. Right now, AI often struggles to adapt to new environments or unexpected changes. This paper shows how to create AI policies that perform consistently well across many scenarios, without needing to constantly adjust. The authors introduce a new approach called IWOCS, which helps AI find the best way to behave even when faced with unexpected challenges. They also test this method and show it can work as well or better than existing solutions. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
