Scaling Laws for Imitation Learning in Single-Agent Games

arXiv:2307.09423v2 Announce Type: replace
Abstract: Imitation Learning (IL) is one of the most widely used methods in machine learning. Yet, many works find it is often unable to fully recover the underlying expert behavior, even in constrained environments like single-agent games. However, none of these works deeply investigate the role of scaling up the model and data size. Inspired by recent work in Natural Language Processing (NLP) where "scaling up" has resulted in increasingly more capable LLMs, we investigate whether …

abstract agent arxiv behavior cs.ai cs.lg environments expert games however imitation learning laws machine machine learning role scaling stat.ml type