Loading Now

Summary of Time-constrained Robust Mdps, by Adil Zouitine et al.


Time-Constrained Robust MDPs

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

     Abstract of paper      PDF of paper


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
This paper proposes a new formulation of robust Markov Decision Processes (MDPs) that better reflects real-world dynamics by considering correlated, multifactorial, and time-dependent disturbances. The traditional rectangularity assumption is relaxed, allowing for more efficient trade-offs between performance and robustness. Three algorithms are introduced, each using varying levels of environmental information, and evaluated on continuous control benchmarks. The results show that these algorithms outperform traditional deep robust RL methods in time-constrained environments while preserving robustness in classical benchmarks.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper is about making artificial intelligence (AI) more reliable and accurate in real-world situations where things don’t always go as planned. Right now, AI systems often make assumptions that aren’t true, which can lead to bad decisions. The researchers developed a new way of thinking about how AI should work that takes into account the complexities of real-life situations. They tested their idea with three different approaches and found that it works better than current methods in some cases.

Keywords

» Artificial intelligence