RECREG: Recovery-based Shielding for Regular Safety Properties in Reinforcement Learning

Key points

• In many real-world scenarios we need to consider formal requirements, such as safety and constraint compliance, to deploy Reinforcement Learning (RL) agents.1
• RecReg is a unified approach that supports different levels of a priori knowledge and safety guarantees.1
• We provide an extensive set of empirical results using the MASA-Safe-RL library, and we outline an extension of RecReg to deep RL domains using SOTA model-based RL.1
• Our empirical results demonstrate that RecReg achieves efficient learning and matches the theory in this paper.1

Sources (1)

• [1]RECREG: Recovery-based Shielding for Regular Safety Properties in Reinforcement Learning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 01:28 PM

  In this paper we introduce RecReg, a safe RL framework capable of enforcing non-Markovian regular safety properties via recovery-based shielding.

  In many real-world scenarios we need to consider formal requirements, such as safety and constraint compliance, to deploy Reinforcement Learning (RL) agents.

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