Safe Reinforcement Learning via Shielding
Abstract
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.
Cite
Text
Alshiekh et al. "Safe Reinforcement Learning via Shielding." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.11797Markdown
[Alshiekh et al. "Safe Reinforcement Learning via Shielding." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/alshiekh2018aaai-safe/) doi:10.1609/AAAI.V32I1.11797BibTeX
@inproceedings{alshiekh2018aaai-safe,
title = {{Safe Reinforcement Learning via Shielding}},
author = {Alshiekh, Mohammed and Bloem, Roderick and Ehlers, Rüdiger and Könighofer, Bettina and Niekum, Scott and Topcu, Ufuk},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2018},
pages = {2669-2678},
doi = {10.1609/AAAI.V32I1.11797},
url = {https://mlanthology.org/aaai/2018/alshiekh2018aaai-safe/}
}