Summary of Principal-agent Reward Shaping in Mdps, by Omer Ben-porat et al.
Principal-Agent Reward Shaping in MDPs
by Omer Ben-Porat, Yishay Mansour, Michal Moshkovitz, Boaz Taitler
First submitted to arxiv on: 30 Dec 2023
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 investigates how rewarding an agent under budget constraints can improve the principal’s utility in a Stackelberg game. The research builds upon existing work on principal-agent problems, including those involving Markov Decision Processes (MDPs). Specifically, the study focuses on a two-player scenario where the principal and agent have distinct reward functions, and the agent chooses a policy to maximize their combined rewards. The findings demonstrate the NP-hardness of the problem and provide polynomial approximation algorithms for certain types of instances, including Stochastic trees and deterministic decision processes with a finite horizon. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how a person or group (the principal) can make sure they get what they want when someone else is making decisions on their behalf. The researcher studied a situation where the person or group has to choose between different rewards, and another party makes choices based on those rewards. They found that by offering extra rewards, the person or group can actually get better results. This is important because it helps us understand how people make decisions when they’re not in charge. |
