Summary of Explicit Lipschitz Value Estimation Enhances Policy Robustness Against Perturbation, by Xulin Chen et al.
Explicit Lipschitz Value Estimation Enhances Policy Robustness Against Perturbation
by Xulin Chen, Ruipeng Liu, Garrett E. Katz
First submitted to arxiv on: 22 Apr 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 novel method to improve the robustness of reinforcement learning (RL) policies trained in simulation for deployment on physical hardware. By combining Lipschitz regularization with Fast Gradient Sign Method, the authors aim to reduce approximation errors in the value function gradients and enhance the policy’s performance under adverse conditions. The approach is evaluated on several continuous control benchmarks, showcasing its effectiveness over prior work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a robotic control task, simulated reinforcement learning (RL) policies often struggle when moved to physical hardware due to modeling error and unpredictable perturbations. A new method combines Lipschitz regularization with the Fast Gradient Sign Method to improve robustness. The approach is tested on several benchmarks, showing it works better than previous methods. |
Keywords
» Artificial intelligence » Regularization » Reinforcement learning
