ARCLE: The Abstraction and Reasoning Corpus Learning Environment for Reinforcement Learning

id:: 2407.20806
Authors:: Hosung Lee, Sejin Kim, Seungpil Lee, Sanha Hwang, Jihwan Lee, Byung-Jun Lee, Sundong Kim
Published:: 2024-07-30
arXiv:: https://arxiv.org/abs/2407.20806
PDF:: https://arxiv.org/pdf/2407.20806
DOI:: N/A
Journal Reference:: N/A
Primary Category:: cs.AI
Categories:: cs.AI, cs.LG
Comment:: Accepted by CoLLAs 2024, Project page: https://github.com/confeitoHS/arcle
github_url:: _

abstract

This paper introduces ARCLE, an environment designed to facilitate reinforcement learning research on the Abstraction and Reasoning Corpus (ARC). Addressing this inductive reasoning benchmark with reinforcement learning presents these challenges: a vast action space, a hard-to-reach goal, and a variety of tasks. We demonstrate that an agent with proximal policy optimization can learn individual tasks through ARCLE. The adoption of non-factorial policies and auxiliary losses led to performance enhancements, effectively mitigating issues associated with action spaces and goal attainment. Based on these insights, we propose several research directions and motivations for using ARCLE, including MAML, GFlowNets, and World Models.

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3. Brief Overview

This paper introduces ARCLE, a reinforcement learning (RL) environment designed for the Abstraction and Reasoning Corpus (ARC) benchmark. ARC presents challenges for RL due to its vast action space, hard-to-reach goals, and variety of tasks. ARCLE, implemented in Gymnasium, aims to address these challenges by providing a tailored environment and a range of tools for RL research. The paper demonstrates that an agent using proximal policy optimization can learn individual ARC tasks within ARCLE, highlighting the effectiveness of non-factorial policies and auxiliary losses. Further research directions are proposed, including the application of MAML, GFlowNets, and World Models.

2. Key Points

ARCLE is a new RL environment for the ARC benchmark, designed to address the challenges of ARC for RL agents.
ARCLE is implemented in Gymnasium and includes various components (envs, loaders, actions, wrappers).
The use of non-factorial policies and auxiliary losses improved the performance of RL agents on ARC tasks within ARCLE.
Proximal Policy Optimization (PPO) was successfully used to train agents on simplified ARC tasks within ARCLE.
The paper proposes several future research directions, including Meta-RL, Generative Flow Networks, and Model-based RL, for tackling the complexities of ARC.

3. Notable Quotes

No notable quotes were identified.

4. Primary Themes

Reinforcement Learning on Abstract Reasoning: The core theme is the application of RL techniques to solve the challenging abstract reasoning problems presented by the ARC benchmark.
Addressing Challenges in RL: The paper focuses on overcoming typical RL difficulties like vast action spaces and hard-to-reach goals, as presented by the ARC benchmark.
ARCLE Environment Design and Functionality: A significant portion discusses the design and capabilities of the ARCLE environment, including its components and features.
Future Research Directions: The paper concludes by suggesting promising avenues for future research leveraging ARCLE, such as Meta-RL, GFlowNets, and model-based RL.