CEDGE applies energy-guided trajectory diffusion to generate adapted samples for off-dynamics offline RL, improving planning and policy learning on the ODRL benchmark.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
The paper proposes an Adaptive Safety Architecture with a mutual-information-based Compound Uncertainty Coefficient, MaxInfoRL policies, and adaptive constraints to actively resolve compound epistemic uncertainty in RL rather than passively enduring it.
citing papers explorer
-
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.
-
Breaking the Epistemic Trap: Active Perception Under Compound Uncertainty
The paper proposes an Adaptive Safety Architecture with a mutual-information-based Compound Uncertainty Coefficient, MaxInfoRL policies, and adaptive constraints to actively resolve compound epistemic uncertainty in RL rather than passively enduring it.