Summary of Robust Markov Decision Processes: a Place Where Ai and Formal Methods Meet, by Marnix Suilen et al.
Robust Markov Decision Processes: A Place Where AI and Formal Methods Meet
by Marnix Suilen, Thom Badings, Eline M. Bovy, David Parker, Nils Jansen
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 This paper explores the limitations of Markov decision processes (MDPs) in modeling sequential decision-making problems, which are widely used in artificial intelligence and formal methods. The traditional MDP assumption that transition probabilities must be precisely known is restrictive, leading to the development of Robust MDPs (RMDPs). RMDPs relax this assumption by defining transition probabilities as belonging to a specific uncertainty set. This paper provides an in-depth survey on RMDPs, covering their fundamentals and extending standard MDP methods like value iteration and policy iteration. The authors also discuss how RMDPs relate to other models and their applications in reinforcement learning and abstraction techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making decision-making systems better by relaxing a big assumption they make. Markov decision processes (MDPs) are commonly used in AI and computer science, but they have a problem: they need exact information about what will happen next. This can be tricky because real-world situations are often uncertain. Robust MDPs (RMDPs) solve this by letting transition probabilities belong to a range of possibilities instead of being exact. The paper explains how RMDPs work and how they relate to other models, as well as their uses in AI and computer science. |
Keywords
» Artificial intelligence » Reinforcement learning
