DOSER detects OOD actions via diffusion-model denoising error and applies selective regularization based on predicted transitions, proving gamma-contraction with performance bounds and outperforming priors on offline RL benchmarks.
arXiv preprint arXiv:2105.08140 , year=
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UNIQ uses split conformal prediction on a multi-expectile ensemble to produce state-adaptive expectiles on top of IQL, yielding consistent gains on D4RL MuJoCo tasks at near-IQL memory cost.
FAN simplifies expressive flow policies and distributional critics in offline RL via single-iteration behavior regularization and single-sample noise conditioning to claim SOTA performance with lower training and inference time.
citing papers explorer
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Beyond Penalization: Diffusion-based Out-of-Distribution Detection and Selective Regularization in Offline Reinforcement Learning
DOSER detects OOD actions via diffusion-model denoising error and applies selective regularization based on predicted transitions, proving gamma-contraction with performance bounds and outperforming priors on offline RL benchmarks.
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UNIQ: Conformal Calibration for Adaptive Conservatism in Offline Reinforcement Learning
UNIQ uses split conformal prediction on a multi-expectile ensemble to produce state-adaptive expectiles on top of IQL, yielding consistent gains on D4RL MuJoCo tasks at near-IQL memory cost.
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Towards Efficient and Expressive Offline RL via Flow-Anchored Noise-conditioned Q-Learning
FAN simplifies expressive flow policies and distributional critics in offline RL via single-iteration behavior regularization and single-sample noise conditioning to claim SOTA performance with lower training and inference time.