Summary of Bisimulation Metric For Model Predictive Control, by Yutaka Shimizu and Masayoshi Tomizuka
Bisimulation metric for Model Predictive Control
by Yutaka Shimizu, Masayoshi Tomizuka
First submitted to arxiv on: 6 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Model-based reinforcement learning has shown promise for improving sample efficiency and decision-making in complex environments, but existing methods face challenges in training stability, robustness to noise, and computational efficiency. This paper proposes 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. BS-MPC improves training stability, robustness against input noise, and computational efficiency by reducing training time. The proposed method is evaluated 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to improve model-based reinforcement learning. Right now, it’s hard to get these models to work well because they can be unstable and don’t do well with noisy data. The new method, called BS-MPC, helps solve these problems by optimizing the “encoder” that helps the model make decisions. This makes the model more stable and better at handling noise. The paper shows that BS-MPC works really well on different types of tasks. |
Keywords
» Artificial intelligence » Encoder » Objective function » Reinforcement learning
