  In the pursuit of artificial general intelligence (AGI), we tackle
Abstraction and Reasoning Corpus (ARC) tasks using a novel two-pronged
approach. We employ the Decision Transformer in an imitation learning paradigm
to model human problem-solving, and introduce an object detection algorithm,
the Push and Pull clustering method. This dual strategy enhances AI's ARC
problem-solving skills and provides insights for AGI progression. Yet, our work
reveals the need for advanced data collection tools, robust training datasets,
and refined model structures. This study highlights potential improvements for
Decision Transformers and propels future AGI research.
