ROAD formulates data mixing as a bi-level optimization problem solved via multi-armed bandit to adaptively balance offline priors and online updates in RL.
Adaptive behav- ior cloning regularization for stable offline-to-online re- inforcement learning.arXiv preprint arXiv:2210.13846
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DF-ExpEnse improves sample efficiency in finetuning diffusion-based robotic policies by filtering diffusion-generated actions with critic ensembles and enabling fleet-level collaboration.
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ROAD: Adaptive Data Mixing for Offline-to-Online Reinforcement Learning via Bi-Level Optimization
ROAD formulates data mixing as a bi-level optimization problem solved via multi-armed bandit to adaptively balance offline priors and online updates in RL.
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DF-ExpEnse: Diffusion Filtered Exploration for Sample Efficient Finetuning
DF-ExpEnse improves sample efficiency in finetuning diffusion-based robotic policies by filtering diffusion-generated actions with critic ensembles and enabling fleet-level collaboration.