Summary of Enhancing Robustness in Deep Reinforcement Learning: a Lyapunov Exponent Approach, by Rory Young et al.
Enhancing Robustness in Deep Reinforcement Learning: A Lyapunov Exponent Approach
by Rory Young, Nicolas Pugeault
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Deep reinforcement learning agents have achieved excellent results in simulated control tasks, but their application to real-world problems is still limited. One reason is that learned policies are not robust to observation noise or adversarial attacks. This paper investigates how deep RL policies perform when faced with a small state perturbation in deterministic continuous control tasks. The study finds that RL policies can be deterministically chaotic, meaning that small changes to the system state have significant effects on subsequent states and rewards. This chaotic behavior has two consequences: it makes it difficult for policies to maintain performance in real-world scenarios, and even robust-looking policies can behave unpredictably. To address this issue, the paper proposes an improvement to the Dreamer V3 architecture, implementing Maximal Lyapunov Exponent regularization to reduce chaotic state dynamics and improve policy resilience. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep reinforcement learning agents are super smart at solving simulated problems, but they struggle when applied to real-life situations. One main reason is that small errors in sensor readings or attacks can cause a huge drop in performance. This paper looks into how well these policies work when there’s a tiny change to the system state. Surprisingly, they found that these policies are very sensitive and can behave chaotically – even small changes can lead to big effects! This means it’s hard for them to perform well in real-world scenarios, and even if they seem robust, they might still behave unpredictably. To fix this, the paper suggests a new way to improve the Dreamer V3 architecture. |
Keywords
* Artificial intelligence * Regularization * Reinforcement learning
