Summary of Analyzing Adversarial Inputs in Deep Reinforcement Learning, by Davide Corsi et al.
Analyzing Adversarial Inputs in Deep Reinforcement Learning
by Davide Corsi, Guy Amir, Guy Katz, Alessandro Farinelli
First submitted to arxiv on: 7 Feb 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 explores the reliability concerns of Deep Reinforcement Learning (DRL) models in real-world applications. State-of-the-art DRL models are vulnerable to adversarial inputs, which can cause unpredictable decisions with potentially severe consequences. To address this issue, the authors propose a novel metric, Adversarial Rate, to classify models based on their susceptibility to perturbations. The paper presents tools and algorithms for computing this metric and demonstrates its impact on the safety of DRL systems. The analysis provides guidelines for mitigating the vulnerability of trained DRL networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how good Deep Reinforcement Learning is, but it’s not perfect. Sometimes, small changes in what we give the model can make it do weird things. This is a problem because some places where we want to use these models are super important and can’t have mistakes. To help with this, the authors made a new way to measure how likely a model is to be fooled by these changes. They also showed how these changes can affect the safety of our models. |
Keywords
* Artificial intelligence * Reinforcement learning
