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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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.
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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.