Enhancing Analogical Reasoning in the Abstraction and Reasoning Corpus via Model-Based RL

• URL: http://arxiv.org/abs/2408.14855v1
• Date: Tue, 27 Aug 2024 08:15:20 GMT
• Title: Enhancing Analogical Reasoning in the Abstraction and Reasoning Corpus via Model-Based RL
• Authors: Jihwan Lee, Woochang Sim, Sejin Kim, Sundong Kim,
• Abstract summary: We show that model-based reinforcement learning is a suitable approach for the task of analogical reasoning. We compare DreamerV3, a model-based RL method, with Proximal Policy Optimization, a model-free RL method. Our results indicate that model-based RL not only outperforms model-free RL in learning and generalizing from single tasks but also shows significant advantages in reasoning across similar tasks.
• Score: 6.143939145442195
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper demonstrates that model-based reinforcement learning (model-based RL) is a suitable approach for the task of analogical reasoning. We hypothesize that model-based RL can solve analogical reasoning tasks more efficiently through the creation of internal models. To test this, 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. Our results indicate that model-based RL not only outperforms model-free RL in learning and generalizing from single tasks but also shows significant advantages in reasoning across similar tasks.

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