Summary of Program-based Strategy Induction For Reinforcement Learning, by Carlos G. Correa and Thomas L. Griffiths and Nathaniel D. Daw
Program-Based Strategy Induction for Reinforcement Learning
by Carlos G. Correa, Thomas L. Griffiths, Nathaniel D. Daw
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers tackle the limitation of traditional machine learning models by exploring non-incremental decision-making strategies. Unlike typical models that continuously update expected rewards, these novel approaches capture idiosyncratic heuristics and strategies exhibited by humans and animals. The team employs Bayesian program induction to discover simple yet effective strategies, focusing on bandit tasks. Their findings include unexpected tactics like asymmetric learning from rewarded and unrewarded trials, adaptive random exploration, and discrete state switching. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way of thinking about how people make decisions. Right now, most machines learn by slowly getting better at something over time. But humans often use shortcuts or special rules to make quick decisions. The researchers are trying to figure out what these shortcuts look like and how they work. They’re using a special kind of computer program to find these shortcuts and see if they can be used to help machines make better decisions too. |
Keywords
* Artificial intelligence * Machine learning
