Reinforcement Learning in Hyperbolic Spaces: Models and Experiments
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914970519732224 |
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| author | Jaćimović, Vladimir Kapić, Zinaid Crnkić, Aladin |
| author_facet | Jaćimović, Vladimir Kapić, Zinaid Crnkić, Aladin |
| contents | We examine five setups where an agent (or two agents) seeks to explore unknown environment without any prior information. Although seemingly very different, all of them can be formalized as Reinforcement Learning (RL) problems in hyperbolic spaces. More precisely, it is natural to endow the action spaces with the hyperbolic metric. We introduce statistical and dynamical models necessary for addressing problems of this kind and implement algorithms based on this framework. Throughout the paper we view RL through the lens of the black-box optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_09466 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning in Hyperbolic Spaces: Models and Experiments Jaćimović, Vladimir Kapić, Zinaid Crnkić, Aladin Machine Learning We examine five setups where an agent (or two agents) seeks to explore unknown environment without any prior information. Although seemingly very different, all of them can be formalized as Reinforcement Learning (RL) problems in hyperbolic spaces. More precisely, it is natural to endow the action spaces with the hyperbolic metric. We introduce statistical and dynamical models necessary for addressing problems of this kind and implement algorithms based on this framework. Throughout the paper we view RL through the lens of the black-box optimization. |
| title | Reinforcement Learning in Hyperbolic Spaces: Models and Experiments |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.09466 |