A Survey of Constraint Formulations in Safe Reinforcement Learning

• URL: http://arxiv.org/abs/2402.02025v2
• Date: Wed, 8 May 2024 00:59:16 GMT
• Title: A Survey of Constraint Formulations in Safe Reinforcement Learning
• Authors: Akifumi Wachi, Xun Shen, Yanan Sui,
• Abstract summary: Safety is critical when applying reinforcement learning to real-world problems. A prevalent safe RL approach is based on a constrained criterion, which seeks to maximize the expected cumulative reward. Despite recent effort to enhance safety in RL, a systematic understanding of the field remains difficult.
• Score: 15.593999581562203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Safety is critical when applying reinforcement learning (RL) to real-world problems. As a result, safe RL has emerged as a fundamental and powerful paradigm for optimizing an agent's policy while incorporating notions of safety. A prevalent safe RL approach is based on a constrained criterion, which seeks to maximize the expected cumulative reward subject to specific safety constraints. Despite recent effort to enhance safety in RL, a systematic understanding of the field remains difficult. This challenge stems from the diversity of constraint representations and little exploration of their interrelations. To bridge this knowledge gap, we present a comprehensive review of representative constraint formulations, along with a curated selection of algorithms designed specifically for each formulation. In addition, we elucidate the theoretical underpinnings that reveal the mathematical mutual relations among common problem formulations. We conclude with a discussion of the current state and future directions of safe reinforcement learning research.

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