Danijar Hafner 2
TalkRL: The Reinforcement Learning Podcast
Dreamer: A MetaRL Agent
The model integrates information over time into Markovian states. We're actually offloading a lot of what's challenging about RRL to the unsupervised model learning objective. And so we don't need rewards to learn which parts are relevant about the state. It can do some sort of meta learning. But yeah, this is almost just an immersion property of using sequence models in RRL which we should have been doing for a long time anyways.
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