Summary of Realizable Continuous-space Shields For Safe Reinforcement Learning, by Kyungmin Kim et al.
Realizable Continuous-Space Shields for Safe Reinforcement Learning
by Kyungmin Kim, Davide Corsi, Andoni Rodriguez, JB Lanier, Benjami Parellada, Pierre Baldi, Cesar Sanchez, Roy Fox
First submitted to arxiv on: 2 Oct 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 proposed shielding approach utilizes realizability, a property that confirms the shield can generate safe actions for any state, to ensure satisfaction of safety requirements in continuous state and action spaces. This method is designed specifically for robotic applications and builds upon existing work in DRL. The approach formally proves verifiability for stateful shields, enabling incorporation of non-Markovian safety requirements like loop avoidance. A navigation problem and a multi-agent particle environment are used to demonstrate the effectiveness of the shielding approach in ensuring safety without compromising policy success rates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep Reinforcement Learning (DRL) has achieved great success, but it can sometimes fail catastrophically. To prevent this, researchers propose using a “shield” that checks and adjusts the agent’s actions to make sure they follow certain rules. For robots, these rules must account for complex systems and ensure new actions don’t stray too far from the original decision. This paper presents the first method specifically designed for robotic applications, ensuring safety without sacrificing success rates. |
Keywords
* Artificial intelligence * Reinforcement learning
