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Representation Matters: Offline Pretraining for Sequential Decision Making
Mengjiao Yang 1 Ofir Nachum 1
Abstract
The recent success of supervised learning meth-
ods on ever larger offline datasets has spurred
interest in the reinforcement learning (RL) field
to investigate whether the same paradigms can
be translated to RL algorithms. This research
area, known as offline RL, has largely focused
on offline policy optimization, ...
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