Summary of Arcle: the Abstraction and Reasoning Corpus Learning Environment For Reinforcement Learning, by Hosung Lee et al.
ARCLE: The Abstraction and Reasoning Corpus Learning Environment for Reinforcement Learning
by Hosung Lee, Sejin Kim, Seungpil Lee, Sanha Hwang, Jihwan Lee, Byung-Jun Lee, Sundong Kim
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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 This paper introduces ARCLE, an environment designed to facilitate reinforcement learning research on the Abstraction and Reasoning Corpus (ARC). The challenges of addressing this inductive reasoning benchmark with reinforcement learning are addressed through the use of a proximal policy optimization agent that can learn individual tasks. Non-factorial policies and auxiliary losses enhance performance by mitigating issues associated with action spaces and goal attainment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a special tool to help researchers study how computers can learn by solving puzzles. The tool, called ARCLE, makes it easier for computers to learn new things by giving them tasks to complete. The researchers used this tool to show that computers can learn individual skills through practice and feedback. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
