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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/2411.01342 |
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| _version_ | 1866915003203846144 |
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| author | Gospodinov, Emiliyan Shaj, Vaisakh Becker, Philipp Geyer, Stefan Neumann, Gerhard |
| author_facet | Gospodinov, Emiliyan Shaj, Vaisakh Becker, Philipp Geyer, Stefan Neumann, Gerhard |
| contents | Developing foundational world models is a key research direction for embodied intelligence, with the ability to adapt to non-stationary environments being a crucial criterion. In this work, we introduce a new formalism, Hidden Parameter-POMDP, designed for control with adaptive world models. We demonstrate that this approach enables learning robust behaviors across a variety of non-stationary RL benchmarks. Additionally, this formalism effectively learns task abstractions in an unsupervised manner, resulting in structured, task-aware latent spaces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_01342 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adaptive World Models: Learning Behaviors by Latent Imagination Under Non-Stationarity Gospodinov, Emiliyan Shaj, Vaisakh Becker, Philipp Geyer, Stefan Neumann, Gerhard Machine Learning Artificial Intelligence Developing foundational world models is a key research direction for embodied intelligence, with the ability to adapt to non-stationary environments being a crucial criterion. In this work, we introduce a new formalism, Hidden Parameter-POMDP, designed for control with adaptive world models. We demonstrate that this approach enables learning robust behaviors across a variety of non-stationary RL benchmarks. Additionally, this formalism effectively learns task abstractions in an unsupervised manner, resulting in structured, task-aware latent spaces. |
| title | Adaptive World Models: Learning Behaviors by Latent Imagination Under Non-Stationarity |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2411.01342 |