Chelsea Finn
@chelseabfinn
Asst Prof of CS & EE @Stanford
Co-founder of Physical Intelligence @physical_int
PhD from @Berkeley_EECS, EECS BS from @MIT
加入 June 2014
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Pretraining a Q-function often doesn’t actually help RL finetuning, compared to initializing Q from scratch.
We find that pretraining Q-functions on data from diverse policies is critical to see improvements from pretraining.
Paper:
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Pretraining has worked remarkably well across domains
We show this doesn’t hold for Q-functions in online RL from a pretrained policy — and propose IPE, a more effective way to learn Q-functions for online RL fine-tuning
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