Summary of Learning Utilities From Demonstrations in Markov Decision Processes, by Filippo Lazzati et al.
Learning Utilities from Demonstrations in Markov Decision Processes
by Filippo Lazzati, Alberto Maria Metelli
First submitted to arxiv on: 25 Sep 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 tackles the challenge of extracting knowledge from human behavior in sequential decision-making problems, specifically addressing risk-sensitive behaviors in stochastic environments. The authors propose a novel model for Markov Decision Processes (MDPs) that explicitly captures an agent’s risk attitude through a utility function, which is critical for many applications. They introduce the Utility Learning (UL) problem as inferring this risk attitude from demonstrations in MDPs and analyze its partial identifiability. Two efficient algorithms are devised for UL in finite-data regimes, with sample complexity analysis. Experimental results validate both the model and algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how people make decisions when they’re not sure what will happen next. Right now, most models of decision-making assume people are completely rational, but that’s not true. In real life, people often take risks or play it safe depending on the situation. This paper creates a new way to think about decision-making that includes this risk factor. They also come up with two ways to figure out what someone’s risk tolerance is just by looking at how they make decisions in different situations. |
