Bisimulation metric for Model Predictive Control

• URL: http://arxiv.org/abs/2410.04553v1
• Date: Sun, 6 Oct 2024 17:12:10 GMT
• Title: Bisimulation metric for Model Predictive Control
• Authors: Yutaka Shimizu, Masayoshi Tomizuka,
• Abstract summary: Bisimulation Metric for Model Predictive Control (BS-MPC) is a novel approach that incorporates bisimulation metric loss in its objective function to directly optimize the encoder. BS-MPC improves training stability, robustness against input noise, and computational efficiency by reducing training time. We evaluate BS-MPC on both continuous control and image-based tasks from the DeepMind Control Suite.
• Score: 44.301098448479195
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model-based reinforcement learning has shown promise for improving sample efficiency and decision-making in complex environments. However, existing methods face challenges in training stability, robustness to noise, and computational efficiency. In this paper, we propose Bisimulation Metric for Model Predictive Control (BS-MPC), a novel approach that incorporates bisimulation metric loss in its objective function to directly optimize the encoder. This time-step-wise direct optimization enables the learned encoder to extract intrinsic information from the original state space while discarding irrelevant details and preventing the gradients and errors from diverging. BS-MPC improves training stability, robustness against input noise, and computational efficiency by reducing training time. We evaluate BS-MPC on both continuous control and image-based tasks from the DeepMind Control Suite, demonstrating superior performance and robustness compared to state-of-the-art baseline methods.

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