ADG uses an ambient DDPM to flag corrupted RL transitions, trains a standard DDPM only on the clean subset, then refines the flagged transitions to produce a recovered dataset that improves offline RL policies.
Uncertainty-based offline reinforcement learning with diversified q-ensemble
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ADG: Ambient Diffusion-Guided Dataset Recovery for Corruption-Robust Offline Reinforcement Learning
ADG uses an ambient DDPM to flag corrupted RL transitions, trains a standard DDPM only on the clean subset, then refines the flagged transitions to produce a recovered dataset that improves offline RL policies.