Summary of 1-2-3-go! Policy Synthesis For Parameterized Markov Decision Processes Via Decision-tree Learning and Generalization, by Muqsit Azeem et al.
1-2-3-Go! Policy Synthesis for Parameterized Markov Decision Processes via Decision-Tree Learning and Generalization
by Muqsit Azeem, Debraj Chakraborty, Sudeep Kanav, Jan Kretinsky, Mohammadsadegh Mohagheghi, Stefanie Mohr, Maximilian Weininger
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Logic in Computer Science (cs.LO); Systems and Control (eess.SY)
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 addresses the scalability issue in probabilistic model checking, specifically for parameterized Markov decision processes (MDPs). The authors propose a learning-based approach to synthesize policies for massive MDPs that are currently out of reach for existing tools. This solution leverages advancements in machine learning and probabilistic verification to provide a scalable method for synthesizing policies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding a way to make it possible to create good plans for very big models, which right now can’t be done with current methods. The authors are trying to use machine learning to help solve this problem. |
Keywords
» Artificial intelligence » Machine learning » Probabilistic model
