Symmetry Teleportation for Accelerated Optimization
Bo Zhao, Nima Dehmamy, et al.
NeurIPS 2022
In recent years, the issue of safety and robustness has become a critical focus for AI research. However, benchmarks for safe reinforcement learning tend to target a specific class of problems and do not offer a holistic set of challenges. In this paper, we propose a benchmark environment for safety-critical problems in deep reinforcement learning with text-based interaction. The contribution of this benchmark is in providing a general framework to incorporate safety constraints in agent interactions, as shown in our five problem gameset; moreover, the games can also be generated automatically to combine the multiple safety problems an agent might face. The source of safety constraints and goals are annotated from real-life examples of safety, and can be adapted to more open problems. Overall, our benchmark of Safety-critical Textworld is a flexible framework to provide a set of tasks to demonstrate a safety base challenges for reinforcement learning agents and aims to help the research community in exploring safety applications in a text-based domain.
Bo Zhao, Nima Dehmamy, et al.
NeurIPS 2022
Soumyadip Ghosh, Mark S. Squillante
WSC 2022
Rui Chen, Sanjeeb Dash, et al.
ICML 2021
Ben Huh, Avinash Baidya
NeurIPS 2022