Bellman Diffusion Models
Fuente:
arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866915590262751232 |
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| author | Schramm, Liam Boularias, Abdeslam |
| author_facet | Schramm, Liam Boularias, Abdeslam |
| contents | Diffusion models have seen tremendous success as generative architectures. Recently, they have been shown to be effective at modelling policies for offline reinforcement learning and imitation learning. We explore using diffusion as a model class for the successor state measure (SSM) of a policy. We find that enforcing the Bellman flow constraints leads to a simple Bellman update on the diffusion step distribution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12163 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bellman Diffusion Models Schramm, Liam Boularias, Abdeslam Machine Learning Robotics Diffusion models have seen tremendous success as generative architectures. Recently, they have been shown to be effective at modelling policies for offline reinforcement learning and imitation learning. We explore using diffusion as a model class for the successor state measure (SSM) of a policy. We find that enforcing the Bellman flow constraints leads to a simple Bellman update on the diffusion step distribution. |
| title | Bellman Diffusion Models |
| topic | Machine Learning Robotics |
| url | https://arxiv.org/abs/2407.12163 |