Summary of Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks, by Eura Nofshin et al.
Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks
by Eura Nofshin, Siddharth Swaroop, Weiwei Pan, Susan Murphy, Finale Doshi-Velez
First submitted to arxiv on: 26 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 introduces Behavior Model Reinforcement Learning (BMRL), a framework for personalized interventions using artificial intelligence (AI) to help individuals stick to their goals. The AI agent must rapidly and interpretably personalize its interventions to understand behavioral changes. BMRL is based on a Markov Decision Process (MDP) belonging to a boundedly rational human agent, allowing the attribution of undesirable policies to maladapted MDP parameters. A class of tractable human models captures fundamental behaviors in frictionful tasks. The paper theoretically and empirically shows that AI planning with these human models can lead to helpful policies on complex humans. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses artificial intelligence to help people make good choices by changing their behavior. It’s like having a personal coach to stay on track with goals. The AI agent needs to quickly understand how the person makes decisions and adapt its own approach. The researchers developed a new way of thinking about this called Behavior Model Reinforcement Learning (BMRL). This allows them to figure out why people make certain choices and develop better strategies to help them succeed. |
Keywords
* Artificial intelligence * Reinforcement learning
