CEDGE applies energy-guided trajectory diffusion to generate adapted samples for off-dynamics offline RL, improving planning and policy learning on the ODRL benchmark.
Dara: Dynamics-aware reward augmentation in offline reinforcement learning
3 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Target-Aligned Bellman Backup (TABB) improves cross-domain offline RL by selecting source transitions according to their contribution to accurate target-domain Bellman target estimation.
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.
citing papers explorer
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Cross-Domain Energy-Guided Diffusion Generation for Off-Dynamics Reinforcement Learning
CEDGE applies energy-guided trajectory diffusion to generate adapted samples for off-dynamics offline RL, improving planning and policy learning on the ODRL benchmark.
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Target-Aligned Bellman Backup for Cross-domain Offline Reinforcement Learning
Target-Aligned Bellman Backup (TABB) improves cross-domain offline RL by selecting source transitions according to their contribution to accurate target-domain Bellman target estimation.
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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.