Summary of Preserving the Privacy Of Reward Functions in Mdps Through Deception, by Shashank Reddy Chirra et al.
Preserving the Privacy of Reward Functions in MDPs through Deception
by Shashank Reddy Chirra, Pradeep Varakantham, Praveen Paruchuri
First submitted to arxiv on: 13 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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 A novel approach to preserving the privacy of sequential decision-making agents is proposed in this paper. By utilizing Markov Decision Processes (MDPs) and inverse reinforcement learning (IRL), the authors aim to safeguard the preferences or rewards of an agent from being inferred by observers. This is particularly crucial in domains like wildlife monitoring, where poachers may exploit knowledge of animal locations to disrupt conservation efforts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers tackle a challenging problem: keeping private the preferences or rewards of a sequential decision-making agent when decisions are observable. The authors focus on planning over a sequence of actions in Markov Decision Processes (MDPs), using the reward function as a proxy for the preference structure to be protected. |
Keywords
» Artificial intelligence » Reinforcement learning
