Summary of Actsafe: Active Exploration with Safety Constraints For Reinforcement Learning, by Yarden As et al.
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning
by Yarden As, Bhavya Sukhija, Lenart Treven, Carmelo Sferrazza, Stelian Coros, Andreas Krause
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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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 proposes a novel reinforcement learning algorithm called ActSafe, which enables agents to learn directly in real-world settings without extensive interactions with the environment. ActSafe uses a well-calibrated probabilistic model of the system and optimistically plans w.r.t. epistemic uncertainty about unknown dynamics while enforcing pessimism w.r.t. safety constraints. The algorithm guarantees safety during learning and obtains near-optimal policies in finite time under certain regularity assumptions on constraints and dynamics. The authors also propose a practical variant that enables safe exploration even in high-dimensional settings like visual control. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ActSafe is a new way for artificial intelligence (AI) to learn without taking too many risks. Right now, AI systems need to spend lots of time interacting with the world to get good at doing things on their own. But what if they could learn from experience and make decisions based on that experience? That’s what ActSafe does – it helps AI systems learn safely and efficiently in real-world situations. |
Keywords
» Artificial intelligence » Probabilistic model » Reinforcement learning
