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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.14653 |
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| _version_ | 1866913439162564608 |
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| author | Yao, Yihang Cen, Zhepeng Ding, Wenhao Lin, Haohong Liu, Shiqi Zhang, Tingnan Yu, Wenhao Zhao, Ding |
| author_facet | Yao, Yihang Cen, Zhepeng Ding, Wenhao Lin, Haohong Liu, Shiqi Zhang, Tingnan Yu, Wenhao Zhao, Ding |
| contents | Offline safe reinforcement learning (RL) aims to train a policy that satisfies constraints using a pre-collected dataset. Most current methods struggle with the mismatch between imperfect demonstrations and the desired safe and rewarding performance. In this paper, we introduce OASIS (cOnditionAl diStributIon Shaping), a new paradigm in offline safe RL designed to overcome these critical limitations. OASIS utilizes a conditional diffusion model to synthesize offline datasets, thus shaping the data distribution toward a beneficial target domain. Our approach makes compliance with safety constraints through effective data utilization and regularization techniques to benefit offline safe RL training. Comprehensive evaluations on public benchmarks and varying datasets showcase OASIS's superiority in benefiting offline safe RL agents to achieve high-reward behavior while satisfying the safety constraints, outperforming established baselines. Furthermore, OASIS exhibits high data efficiency and robustness, making it suitable for real-world applications, particularly in tasks where safety is imperative and high-quality demonstrations are scarce. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_14653 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | OASIS: Conditional Distribution Shaping for Offline Safe Reinforcement Learning Yao, Yihang Cen, Zhepeng Ding, Wenhao Lin, Haohong Liu, Shiqi Zhang, Tingnan Yu, Wenhao Zhao, Ding Machine Learning Offline safe reinforcement learning (RL) aims to train a policy that satisfies constraints using a pre-collected dataset. Most current methods struggle with the mismatch between imperfect demonstrations and the desired safe and rewarding performance. In this paper, we introduce OASIS (cOnditionAl diStributIon Shaping), a new paradigm in offline safe RL designed to overcome these critical limitations. OASIS utilizes a conditional diffusion model to synthesize offline datasets, thus shaping the data distribution toward a beneficial target domain. Our approach makes compliance with safety constraints through effective data utilization and regularization techniques to benefit offline safe RL training. Comprehensive evaluations on public benchmarks and varying datasets showcase OASIS's superiority in benefiting offline safe RL agents to achieve high-reward behavior while satisfying the safety constraints, outperforming established baselines. Furthermore, OASIS exhibits high data efficiency and robustness, making it suitable for real-world applications, particularly in tasks where safety is imperative and high-quality demonstrations are scarce. |
| title | OASIS: Conditional Distribution Shaping for Offline Safe Reinforcement Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.14653 |