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Dibya Ghosh

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Articles

Robotic Offline RL from Internet Videos via Value-Function Pre-Training

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By Derek Guo, Dibya Ghosh, Anikait Singh, Chethan Bhateja
| huggingface.co
Robotic Offline RL from Internet Videos via Value-Function Pre-Training Authors: Chethan Bhateja , Dibya Ghosh , Anikait Singh , Manan Tomar , Quan Vuong , Yevgen Chebotar , Sergey Levine , Aviral Kumar Abstract Pre-training on Internet data has proven to be a key ingredient for broad generalization in many modern ML systems. What would it take to enable such capabilities in robotic reinforcement learning (RL)?
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[2309.13041] Robotic Offline RL from Internet Videos via Value-Function Pre-Training

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By Dibya Ghosh, Derek Guo, Anikait Singh, Manan Tomar
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (532) Issue with article?

Why generalization in RL is difficult: epistemic POMDPs and implicit partial observability - ΑΙhub

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By Dibya Ghosh, Lucy Smith, Puze Liu, Davide Tateo
| aihub.org
By Dibya Ghosh Many experimental works have observed that generalization in deep RL appears to be difficult: although RL agents can learn to perform very complex tasks, they don’t seem to generalize over diverse task distributions as well as the excellent generalization of supervised deep nets might lead us to expect. In this blog post, we will aim to explain why generalization in RL is fundamentally harder, and indeed more difficult even in theory.
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