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.
Pessimistic bootstrapping for uncertainty-driven offline reinforcement learning.arXiv preprint arXiv:2202.11566
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QHyer replaces return-to-go with a state-conditioned Q-estimator and adds a gated hybrid attention-mamba backbone to achieve state-of-the-art performance in offline goal-conditioned RL on both Markovian and non-Markovian datasets.
VIPO improves model-based offline RL by minimizing value function inconsistency between direct data estimates and model predictions, achieving SOTA results on D4RL and NeoRL benchmarks.
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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QHyer: Q-conditioned Hybrid Attention-mamba Transformer for Offline Goal-conditioned RL
QHyer replaces return-to-go with a state-conditioned Q-estimator and adds a gated hybrid attention-mamba backbone to achieve state-of-the-art performance in offline goal-conditioned RL on both Markovian and non-Markovian datasets.
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VIPO: Value Function Inconsistency Penalized Offline Reinforcement Learning
VIPO improves model-based offline RL by minimizing value function inconsistency between direct data estimates and model predictions, achieving SOTA results on D4RL and NeoRL benchmarks.