A Concise Mathematical Description of Active Inference in Discrete Time
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916740742512640 |
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| author | van Oostrum, Jesse Langer, Carlotta Ay, Nihat |
| author_facet | van Oostrum, Jesse Langer, Carlotta Ay, Nihat |
| contents | In this paper we present a concise mathematical description of active inference in discrete time. The main part of the paper serves as a basic introduction to the topic, including a detailed example of the action selection mechanism. The appendix discusses the more subtle mathematical details, targeting readers who have already studied the active inference literature but struggle to make sense of the mathematical details and derivations. Throughout, we emphasize precise and standard mathematical notation, ensuring consistency with existing texts and linking all equations to widely used references on active inference. Additionally, we provide Python code that implements the action selection and learning mechanisms described in this paper and is compatible with pymdp environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07726 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Concise Mathematical Description of Active Inference in Discrete Time van Oostrum, Jesse Langer, Carlotta Ay, Nihat Machine Learning Neurons and Cognition In this paper we present a concise mathematical description of active inference in discrete time. The main part of the paper serves as a basic introduction to the topic, including a detailed example of the action selection mechanism. The appendix discusses the more subtle mathematical details, targeting readers who have already studied the active inference literature but struggle to make sense of the mathematical details and derivations. Throughout, we emphasize precise and standard mathematical notation, ensuring consistency with existing texts and linking all equations to widely used references on active inference. Additionally, we provide Python code that implements the action selection and learning mechanisms described in this paper and is compatible with pymdp environments. |
| title | A Concise Mathematical Description of Active Inference in Discrete Time |
| topic | Machine Learning Neurons and Cognition |
| url | https://arxiv.org/abs/2406.07726 |