# Build a curious rocket: the hands-on cpomdp tutorial for the Applied Active Inference Symposium Presenter: Dan Corva Contribution: Interactive Workshop Public ID: talk-80262f867d75698a ## Submitted abstract In the 1960s, Rudolf Kalman wrote down a filter that would go on to navigate Apollo to the moon. It has been the workhorse of continuous-state estimation ever since, and if you have ever built a continuous active inference agent in the linear-Gaussian regime, you have built one too, whether you intended to or not. Bolt a goal onto it and you get a very good controller that is not curious in the slightest. Koudahl, Kouw and de Vries proved why in 2021: under a fixed sensor the epistemic value is identical for every action, so expected free energy quietly reduces to Linear Quadratic Regulator (LQR). In this workshop, in about ten lines of Python that will look familiar to anyone who has used pymdp, we are going to build a rocket! Then, we add the interesting part, curiosity, by making a surgical, biologically realistic change: adding sensor noise that varies based on the rocket's position. We will see how the flight path bends when we add artifacts like a navigation beacon or a blackout zone, and explore the science as to why. You will write every line yourself in a Colab notebook and do not worry there will be plenty of scaffolding from the cpomdp team. Code hints will drop in on a timer, and a reference cell for anyone who gets stuck or would rather just see the finished steps. We will build this in three steps: the 1960 rocket, a nav beacon it can fix its position by, and a blackout zone. Everything we use runs on the PyPI release of cpomdp and anything you build in the notebook is yours to keep and continue working on. Prerequisites: comfortable editing a few lines of Python. Familiarity with pymdp helps but is not required. Nothing to install; a browser and a google account is enough. If you would like to use your own IDE feel free but we may not be able to provide 1-on-1 support if you get stuck. Source: [submitted source]() Abstract SHA256: 8d2e1b9353c6c0730a9a5fd5a253822fbfc29c8147f8ee62cb7bdd1916f01896 ## Organizer review checklist - [ ] Confirm the submitted title, presenter, and abstract with the presenter. - [ ] Confirm contribution format and scheduling needs. - [ ] Confirm public permissions for any additional material before publication. - [ ] Prepare discussion questions grounded in the submitted abstract. ## Public learning resources These approved event resources are starting points; topic relevance and author identity have not been inferred. - Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology - Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations - GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER - Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation - pymdp: https://github.com/infer-actively/pymdp - Reproducible publication template: https://github.com/docxology/template OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Build%20a%20curious%20rocket%3A%20the%20hands-on%20cpomdp%20tutorial%20for%20the%20Applied%20Active%20Inference%20Symposium