MODIP fine-tunes diffusion policies offline-to-online by training a world model, running MPC with terminal state values inside it to create targets, and using policy-independent TD critics, yielding gains over BC on D4RL and RoboMimic tasks.
Yuhang Wang, Hanwei Guo, Sizhe Wang, Long Qian, and Xuguang Lan
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PO-MPC unifies prior MPPI-based RL approaches under a single KL-regularized framework that uses the planner distribution as a prior, with new variations yielding performance gains in experiments.
MBDPO reformulates policy optimization as a diffusion process over searched trajectories in latent world models to reduce misalignment between search and value learning.
citing papers explorer
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MODIP: Efficient Model-Based Optimization for Diffusion Policies
MODIP fine-tunes diffusion policies offline-to-online by training a world model, running MPC with terminal state values inside it to create targets, and using policy-independent TD critics, yielding gains over BC on D4RL and RoboMimic tasks.
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A KL-regularization Framework for Learning to Plan with Adaptive Priors
PO-MPC unifies prior MPPI-based RL approaches under a single KL-regularized framework that uses the planner distribution as a prior, with new variations yielding performance gains in experiments.
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Scaling World-Model Reinforcement Learning Through Diffusion Policy Optimization
MBDPO reformulates policy optimization as a diffusion process over searched trajectories in latent world models to reduce misalignment between search and value learning.