
DMEMM Enhances Offline RL Planning
DMEMM is a novel diffusion-based planning method for offline reinforcement learning that modulates training by incorporating environment transition dynamics and reward functions. It ensures trajectory consistency with real environments, addressing limitations in conventional diffusion models. Experiments demonstrate state-of-the-art performance.






