Summary of Uduc: An Uncertainty-driven Approach For Learning-based Robust Control, by Yuan Zhang et al.
UDUC: An Uncertainty-driven Approach for Learning-based Robust Control
by Yuan Zhang, Jasper Hoffmann, Joschka Boedecker
First submitted to arxiv on: 4 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper proposes a new approach to learning-based control in model predictive control (MPC) and reinforcement learning (RL). It introduces the Uncertainty-Driven Robust Control (UDUC) loss function for training probabilistic ensemble (PE) models, which are capable of capturing uncertainty and scaling well in high-dimensional control scenarios. However, PE models are prone to mode collapse, leading to non-robust control when faced with environments that differ slightly from the training set. The authors analyze the robustness of the UDUCT loss through robust optimization and evaluate its performance on the Real-world Reinforcement Learning (RWRL) benchmark, which involves significant environmental mismatches between training and testing environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to control systems using machine learning. It proposes an “uncertainty-driven” approach that helps models be more robust when things don’t go exactly as expected. The authors test their method on a challenging problem called Real-world Reinforcement Learning, where the goal is to get robots or machines to do tasks even when the environment is different from what they were trained in. |
Keywords
» Artificial intelligence » Loss function » Machine learning » Optimization » Reinforcement learning
