Diffusion Model Predictive Control
<|reference_start|>Diffusion Model Predictive Control: We propose Diffusion Model Predictive Control (D-MPC), a novel MPC approach that learns a multi-step action proposal and a multi-step dynamics model, both using diffusion models, and combines them for use in online MPC. On the popular D4RL benchmark, we show performance that is significantly better than existing model-based offline planning methods using MPC and competitive with state-of-the-art (SOTA) model-based and model-free reinforcement learning methods. We additionally illustrate D-MPC's ability to optimize novel reward functions at run time and adapt to novel dynamics, and highlight its advantages compared to existing diffusion-based planning baselines.<|reference_end|>
@article{zhou2024diffusion,
    title={Diffusion Model Predictive Control},
    author={Guangyao Zhou, Sivaramakrishnan Swaminathan, Rajkumar Vasudeva Raju,
  J. Swaroop Guntupalli, Wolfgang Lehrach, Joseph Ortiz, Antoine Dedieu, Miguel
  L'azaro-Gredilla, Kevin Murphy},
    journal={arXiv preprint arXiv:2410.05364},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05364},
    primaryClass={cs.LG cs.AI}
}