On Neural Consolidation for Transfer in Reinforcement Learning


October 5, 2022


Pub Type:



IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning


Carlos Aguilar Melchor, Valentin Guillet, Dennis G. Wilson, Emmanuel Rachelson


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.

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