On Neural Consolidation for Transfer in Reinforcement Learning
Abstract
Although transfer learning is considered to be a milestone in deep reinforcement learning, the mechanisms behind it are still poorly understood. In particular, predicting if knowledge can be transferred between two given tasks is still an unresolved problem. In this work, we explore the use of network distillation as a feature extraction method to better understand the context in which transfer can occur. Notably, we show that distillation does not prevent knowledge transfer, including when transferring from multiple tasks to a new one, and we compare these results with transfer without prior distillation. We focus our work on the Atari benchmark due to the variability between different games, but also to their similarities in terms of visual features.
Keywords
Cite
@article{arxiv.2210.02240,
title = {On Neural Consolidation for Transfer in Reinforcement Learning},
author = {Valentin Guillet and Dennis G. Wilson and Carlos Aguilar-Melchor and Emmanuel Rachelson},
journal= {arXiv preprint arXiv:2210.02240},
year = {2022}
}
Comments
Published at the IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (IEEE ADPRL), 2022