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Safe Reinforcement Learning Using Probabilistic Shields (Invited Paper)

Authors Nils Jansen, Bettina Könighofer, Sebastian Junges, Alex Serban, Roderick Bloem

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Nils Jansen
  • Radboud University, Nijmegen, The Netherlands
Bettina Könighofer
  • Graz University of Technology, Austria
Sebastian Junges
  • University of California, Berkeley, CA, USA
Alex Serban
  • Radboud University, Nijmegen, The Netherlands
Roderick Bloem
  • Graz University of Technology, Austria

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Nils Jansen, Bettina Könighofer, Sebastian Junges, Alex Serban, and Roderick Bloem. Safe Reinforcement Learning Using Probabilistic Shields (Invited Paper). In 31st International Conference on Concurrency Theory (CONCUR 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 171, pp. 3:1-3:16, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)


This paper concerns the efficient construction of a safety shield for reinforcement learning. We specifically target scenarios that incorporate uncertainty and use Markov decision processes (MDPs) as the underlying model to capture such problems. Reinforcement learning (RL) is a machine learning technique that can determine near-optimal policies in MDPs that may be unknown before exploring the model. However, during exploration, RL is prone to induce behavior that is undesirable or not allowed in safety- or mission-critical contexts. We introduce the concept of a probabilistic shield that enables RL decision-making to adhere to safety constraints with high probability. We employ formal verification to efficiently compute the probabilities of critical decisions within a safety-relevant fragment of the MDP. These results help to realize a shield that, when applied to an RL algorithm, restricts the agent from taking unsafe actions, while optimizing the performance objective. We discuss tradeoffs between sufficient progress in the exploration of the environment and ensuring safety. In our experiments, we demonstrate on the arcade game PAC-MAN and on a case study involving service robots that the learning efficiency increases as the learning needs orders of magnitude fewer episodes.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Reinforcement learning
  • Theory of computation → Verification by model checking
  • Theory of computation → Reinforcement learning
  • Computing methodologies → Markov decision processes
  • Safe Reinforcement Learning
  • Formal Verification
  • Safe Exploration
  • Model Checking
  • Markov Decision Process


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