Safe Reinforcement Learning via Shielding


  • Mohammed Alshiekh University of Texas at Austin
  • Roderick Bloem Graz University of Technology
  • Rüdiger Ehlers University of Bremen and DFKI GmbH
  • Bettina Könighofer Graz University of Technology, Institute for Applied Information Processing and Communications
  • Scott Niekum University of Texas at Austin
  • Ufuk Topcu University of Texas at Austin



Reinforcement Learning, Formal Methods


Reinforcement learning algorithms discover policies that maximize reward, but do not necessarily guarantee safety during learning or execution phases. We introduce a new approach to learn optimal policies while enforcing properties expressed in temporal logic. To this end, given the temporal logic specification that is to be obeyed by the learning system, we propose to synthesize a reactive system called a shield. The shield monitors the actions from the learner and corrects them only if the chosen action causes a violation of the specification. We discuss which requirements a shield must meet to preserve the convergence guarantees of the learner. Finally, we demonstrate the versatility of our approach on several challenging reinforcement learning scenarios.




How to Cite

Alshiekh, M., Bloem, R., Ehlers, R., Könighofer, B., Niekum, S., & Topcu, U. (2018). Safe Reinforcement Learning via Shielding. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).