Web: http://arxiv.org/abs/2201.12122

Jan. 31, 2022, 2:11 a.m. | Machel Reid, Yutaro Yamada, Shixiang Shane Gu

cs.LG updates on arXiv.org arxiv.org

Fine-tuning reinforcement learning (RL) models has been challenging because
of a lack of large scale off-the-shelf datasets as well as high variance in
transferability among different environments. Recent work has looked at
tackling offline RL from the perspective of sequence modeling with improved
results as result of the introduction of the Transformer architecture. However,
when the model is trained from scratch, it suffers from slow convergence
speeds. In this paper, we look to take advantage of this formulation of
reinforcement …

arxiv learning reinforcement learning wikipedia

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