Summary of Enhancing Analogical Reasoning in the Abstraction and Reasoning Corpus Via Model-based Rl, by Jihwan Lee et al.
Enhancing Analogical Reasoning in the Abstraction and Reasoning Corpus via Model-Based RL
by Jihwan Lee, Woochang Sim, Sejin Kim, Sundong Kim
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Logic in Computer Science (cs.LO)
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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 explores the application of model-based reinforcement learning (RL) to analogical reasoning, a task requiring the creation of internal models. We compared DreamerV3, a model-based RL method, with Proximal Policy Optimization, a model-free RL method, on the Abstraction and Reasoning Corpus (ARC) tasks. The results show that model-based RL outperforms model-free RL in learning and generalizing from single tasks, as well as reasoning across similar tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to use machines to get better at solving problems that involve recognizing patterns between different things. We tried using a special way of training machines called “model-based reinforcement learning” on some tricky math problems. Our results showed that this method is really good at not just solving one problem, but also figuring out how to solve similar problems in the future. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
