SDGD uses cost-conditioned classifier-free guidance plus reward guidance with feasible trajectory relabeling to generate safe high-reward trajectories that adapt to changing safety budgets in offline RL.
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Advantage-guided diffusion (SAG and EAG) steers sampling in diffusion world models to higher-advantage trajectories, enabling policy improvement and better sample efficiency on MuJoCo tasks.
ALGD augments the Lagrangian to locally convexify the energy landscape in diffusion models, stabilizing safe RL training and generation without changing optimal policies.
citing papers explorer
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Decoupled Guidance Diffusion for Adaptive Offline Safe Reinforcement Learning
SDGD uses cost-conditioned classifier-free guidance plus reward guidance with feasible trajectory relabeling to generate safe high-reward trajectories that adapt to changing safety budgets in offline RL.
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Advantage-Guided Diffusion for Model-Based Reinforcement Learning
Advantage-guided diffusion (SAG and EAG) steers sampling in diffusion world models to higher-advantage trajectories, enabling policy improvement and better sample efficiency on MuJoCo tasks.
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How Does the Lagrangian Guide Safe Reinforcement Learning through Diffusion Models?
ALGD augments the Lagrangian to locally convexify the energy landscape in diffusion models, stabilizing safe RL training and generation without changing optimal policies.