Nightmare Dreamer: Dreaming About Unsafe States And Planning Ahead
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909984477937664 |
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| author | Oseni, Oluwatosin Wang, Shengjie Zhu, Jun Corah, Micah |
| author_facet | Oseni, Oluwatosin Wang, Shengjie Zhu, Jun Corah, Micah |
| contents | Reinforcement Learning (RL) has shown remarkable success in real-world applications, particularly in robotics control. However, RL adoption remains limited due to insufficient safety guarantees. We introduce Nightmare Dreamer, a model-based Safe RL algorithm that addresses safety concerns by leveraging a learned world model to predict potential safety violations and plan actions accordingly. Nightmare Dreamer achieves nearly zero safety violations while maximizing rewards. Nightmare Dreamer outperforms model-free baselines on Safety Gymnasium tasks using only image observations, achieving nearly a 20x improvement in efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_04686 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Nightmare Dreamer: Dreaming About Unsafe States And Planning Ahead Oseni, Oluwatosin Wang, Shengjie Zhu, Jun Corah, Micah Machine Learning Robotics Reinforcement Learning (RL) has shown remarkable success in real-world applications, particularly in robotics control. However, RL adoption remains limited due to insufficient safety guarantees. We introduce Nightmare Dreamer, a model-based Safe RL algorithm that addresses safety concerns by leveraging a learned world model to predict potential safety violations and plan actions accordingly. Nightmare Dreamer achieves nearly zero safety violations while maximizing rewards. Nightmare Dreamer outperforms model-free baselines on Safety Gymnasium tasks using only image observations, achieving nearly a 20x improvement in efficiency. |
| title | Nightmare Dreamer: Dreaming About Unsafe States And Planning Ahead |
| topic | Machine Learning Robotics |
| url | https://arxiv.org/abs/2601.04686 |