GUARD: A Safe Reinforcement Learning Benchmark

Weiye Zhao, Yifan Sun, Feihan Li, Rui Chen, Ruixuan Liu, Tianhao Wei, Changliu Liu

Abstract: Due to the trial-and-error nature, it is typically challenging to apply RL algorithms to safety-critical real-world applications, such as autonomous driving, human-robot interaction, robot manipulation, etc, where such errors are not tolerable. Recently, safe RL (i.e. constrained RL) has emerged rapidly in the literature, in which the agents explore the environment while satisfying constraints. Due to the diversity of algorithms and tasks, it remains difficult to compare existing safe RL algorithms. To fill that gap, we introduce GUARD, a Generalized Unified SAfe Reinforcement Learning Development Benchmark. GUARD has several advantages compared to existing benchmarks. First, GUARD is a generalized benchmark with a wide variety of RL agents, tasks, and safety constraint specifications. Second, GUARD comprehensively covers state-of-the-art safe RL algorithms with self-contained implementations. Third, GUARD is highly customizable in tasks and algorithms. We present a comparison of state-of-the-art on-policy safe RL algorithms in various task settings using GUARD and establish baselines that future work can build on.

Submission Length: Long submission (more than 12 pages of main content)

Code: https://github.com/intelligent-control-lab/guard

Assigned Action Editor: ~Vikas_Sindhwani1

Submission Number: 1886