June 11, 2024, 4:46 a.m. | Donghu Kim, Hojoon Lee, Kyungmin Lee, Dongyoon Hwang, Jaegul Choo

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.06037v1 Announce Type: new
Abstract: Recently, various pre-training methods have been introduced in vision-based Reinforcement Learning (RL). However, their generalization ability remains unclear due to evaluations being limited to in-distribution environments and non-unified experimental setups. To address this, we introduce the Atari Pre-training Benchmark (Atari-PB), which pre-trains a ResNet-50 model on 10 million transitions from 50 Atari games and evaluates it across diverse environment distributions. Our experiments show that pre-training objectives focused on learning task-agnostic features (e.g., identifying objects and …

arxiv cs.ai cs.cv cs.lg pre-training reinforcement reinforcement learning training type vision

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