OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning
  Research
Jiaming Ji Jiayi Zhou Borong Zhang Juntao Dai Xuehai Pan Ruiyang Sun Weidong Huang Yiran Geng Mickel Liu Yaodong Yang
AI systems empowered by reinforcement learning (RL) algorithms harbor the
immense potential to catalyze societal advancement, yet their deployment is
often impeded by significant safety concerns. Particularly in safety-critical
applications, researchers have raised concerns about unintended harms or unsafe
behaviors of unaligned RL agents. The philosophy of safe reinforcement learning
(SafeRL) is to align RL agents with harmless intentions and safe behavioral
patterns. In SafeRL, agents learn to develop optimal policies by receiving
feedback from the environment, while also fulfilling the requirement of
minimizing the risk of unintended harm or unsafe behavior. However, due to the
intricate nature of SafeRL algorithm implementation, combining methodologies
across various domains presents a formidable challenge. This had led to an
absence of a cohesive and efficacious learning framework within the
contemporary SafeRL research milieu. In this work, we introduce a foundational
framework designed to expedite SafeRL research endeavors. Our comprehensive
framework encompasses an array of algorithms spanning different RL domains and
places heavy emphasis on safety elements. Our efforts are to make the
SafeRL-related research process more streamlined and efficient, therefore
facilitating further research in AI safety. Our project is released at:
https://github.com/PKU-Alignment/omnisafe.