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SCERL

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Description

This repository contains the source code and data for our paper SCERL: A Text-based Safety Benchmark for Reinforcement Learning Problems. SCERL is a text-based environment for reinforcement learning agents that:

  • provides a framework for genereting safety problems representing key safety challenges such as negative side effect, scalable oversight and safe exploration
  • includes a pre-generated set of text-based games with safety constraints in order to spoor research in safe and text-based reinforcement learning (see dataset/safety_games).

Main Contributors

Lan Hoang, Shivam Ratnakar, Nicolas Galichet, Akifumi Wachi, Keerthiram Murugesan, Songtao Lu, Mattia Atzeni, Michael Katz, Subhajit Chaudhury