Bisimulation metric for Model Predictive Control
<|reference_start|>Bisimulation metric for Model Predictive Control: 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.<|reference_end|>
@article{shimizu2024bisimulation,
    title={Bisimulation metric for Model Predictive Control},
    author={Yutaka Shimizu and Masayoshi Tomizuka},
    journal={arXiv preprint arXiv:2410.04553},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04553},
    primaryClass={cs.LG cs.SY eess.SY}
}