Summary of Xsrl: Safety-aware Explainable Reinforcement Learning — Safety As a Product Of Explainability, by Risal Shahriar Shefin et al.
xSRL: Safety-Aware Explainable Reinforcement Learning – Safety as a Product of Explainability
by Risal Shahriar Shefin, Md Asifur Rahman, Thai Le, Sarra Alqahtani
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Reinforcement learning (RL) has made significant progress in simulated environments, but deploying RL agents in real-world systems like autonomous vehicles and medical devices demands higher safety and transparency standards. Safe RL algorithms have been developed to optimize task performance and safety constraints, but when errors occur, it’s essential that agents can explain their actions to human operators. Explainability is critical for building trust in RL systems by providing actionable insights into the agent’s decision-making process. Current ML methods are inadequate for safety-critical RL applications, so we propose xSRL, a framework that integrates local and global explanations to provide comprehensive understanding of RL agents’ behavior. xSRL also enables developers to identify policy vulnerabilities through adversarial attacks, offering tools to debug and patch agents without retraining. Our experiments demonstrate xSRL’s effectiveness in increasing safety in RL systems, making them more reliable for real-world deployment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning (RL) is a type of artificial intelligence that helps machines make decisions. In video games, RL works well, but it’s not so good when used in real-life situations like self-driving cars or medical devices. These devices need to be very safe and explain what they’re doing. Right now, there are limited ways to do this for RL systems. We’ve created a new way called xSRL that helps machines make better decisions by explaining why they did something. This makes it easier to trust the machines with important tasks. |
Keywords
» Artificial intelligence » Reinforcement learning
