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arXiv · 2410.05364

Diffusion Model Predictive Control

Guangyao Zhou·Sivaramakrishnan Swaminathan·Rajkumar Vasudeva Raju·J. Swaroop Guntupalli·Wolfgang Lehrach·Joseph Ortiz·Antoine Dedieu·Miguel Lázaro-Gredilla·Kevin Murphy
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Abstract

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 (e.g. MBOP) 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.

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Guangyao Zhou, Sivaramakrishnan Swaminathan, Rajkumar Vasudeva Raju, J. Swaroop Guntupalli, Wolfgang Lehrach, Joseph Ortiz, Antoine Dedieu, Miguel Lázaro-Gredilla, Kevin Murphy. 2024-10-07. Diffusion Model Predictive Control. https://arxiv.org/abs/2410.05364

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