GUARD: A Safe Reinforcement Learning Benchmark
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914955417092096 |
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| author | Zhao, Weiye Sun, Yifan Li, Feihan Chen, Rui Liu, Ruixuan Wei, Tianhao Liu, Changliu |
| author_facet | Zhao, Weiye Sun, Yifan Li, Feihan Chen, Rui Liu, Ruixuan Wei, Tianhao Liu, Changliu |
| contents | 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 safe RL algorithms in various task settings using GUARD and establish baselines that future work can build on. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_13681 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | GUARD: A Safe Reinforcement Learning Benchmark Zhao, Weiye Sun, Yifan Li, Feihan Chen, Rui Liu, Ruixuan Wei, Tianhao Liu, Changliu Machine Learning Artificial Intelligence Robotics 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 safe RL algorithms in various task settings using GUARD and establish baselines that future work can build on. |
| title | GUARD: A Safe Reinforcement Learning Benchmark |
| topic | Machine Learning Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2305.13681 |