  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.
