Summary of Leveraging Approximate Model-based Shielding For Probabilistic Safety Guarantees in Continuous Environments, by Alexander W. Goodall et al.
Leveraging Approximate Model-based Shielding for Probabilistic Safety Guarantees in Continuous Environments
by Alexander W. Goodall, Francesco Belardinelli
First submitted to arxiv on: 1 Feb 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 This paper extends the approximate model-based shielding (AMBS) framework to continuous state and action spaces, a challenging area for classical shielding approaches. The researchers use Safety Gym as their testbed, allowing them to compare AMBS with popular constrained reinforcement learning algorithms. They provide strong probabilistic safety guarantees for the continuous setting, addressing a long-standing limitation of traditional shielding methods. To improve convergence stability, they propose two novel penalty techniques that modify the policy gradient. These innovations enable more effective and safe deployment of RL in complex environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research makes it possible to use a technique called “shielding” in situations where things can move continuously, like in a video game or robot environment. Shielding helps keep agents (like robots) safe while they learn how to do tasks. The scientists took an existing method that’s good for simple cases and made it work for more complex situations. They tested this new method using Safety Gym, which is a special place where they can compare their approach with other popular methods. They also came up with two new ways to make the agent learn faster and more safely. |
Keywords
* Artificial intelligence * Reinforcement learning
