Summary of On Robust Reinforcement Learning with Lipschitz-bounded Policy Networks, by Nicholas H. Barbara et al.
On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networks
by Nicholas H. Barbara, Ruigang Wang, Ian R. Manchester
First submitted to arxiv on: 19 May 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 This study explores the benefits of policy networks in deep reinforcement learning by investigating policy parameterizations that naturally satisfy constraints on their Lipschitz bound. The empirical performance and robustness of these policies are analyzed on two representative problems: pendulum swing-up and Atari Pong. The results show that policy networks with smaller Lipschitz bounds are more robust to disturbances, random noise, and targeted adversarial attacks than unconstrained policies composed of vanilla multi-layer perceptrons or convolutional neural networks. However, the structure of the Lipschitz layer is crucial, as the widely-used method of spectral normalization can be too conservative and negatively impact clean performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make policy networks in deep reinforcement learning more robust. It tries different ways to limit the size of a policy’s “lip” (Lipschitz bound). The researchers test these methods on two games: pendulum swing-up and Atari Pong. They find that policies with smaller lips are more resistant to mistakes, noise, and attacks than regular policies. However, they also show that the way you make this limitation can matter a lot. |
Keywords
» Artificial intelligence » Reinforcement learning
