Summary of Anomalous State Sequence Modeling to Enhance Safety in Reinforcement Learning, by Leen Kweider et al.
Anomalous State Sequence Modeling to Enhance Safety in Reinforcement Learning
by Leen Kweider, Maissa Abou Kassem, Ubai Sandouk
First submitted to arxiv on: 29 Jul 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 The proposed safe reinforcement learning (RL) approach, called Safe Reinforcement Learning with Anomalous State Sequences (AnoSeqs), enhances RL safety by utilizing anomalous state sequences in changing environments. The method consists of two stages: training an agent offline to collect safe state sequences and building an anomaly detection model to detect potentially unsafe state sequences in a target environment. The estimated risk from the anomaly detection model is used to train a risk-averse RL policy, which adjusts the reward function to penalize the agent for visiting anomalous states deemed unsafe. This approach successfully learns safer policies on multiple safety-critical benchmarking environments, including self-driving cars. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence (AI) is being used in more and more decision-making applications. However, this requires ensuring that AI decisions are safe and reliable. The problem gets even harder when there are many unknown observations. To solve this challenge, scientists have developed a new way to make AI safer called Safe Reinforcement Learning with Anomalous State Sequences (AnoSeqs). This method trains an agent offline to learn what is safe, then uses that information to detect potentially dangerous situations in the real world. The algorithm adjusts its decisions based on how likely it is to get into trouble. Tests show that this approach makes AI more careful and safe. |
Keywords
» Artificial intelligence » Anomaly detection » Reinforcement learning
