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Python developers, researchers, and engineers implementing continuous-state Active Inference models.
A tested, documented continuous-state Active Inference library in Python.
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cpomdp is an open-source Python library for continuous-state Active Inference. It provides factor-graph message passing, Kalman and reference filtering, state-dependent observation and process noise, certifiable tolerances, and matched classical control baselines.
cpomdp brings rigorous continuous-state Active Inference to Python. Agents maintain Gaussian beliefs over continuous latent states, update via message passing on Forney-style factor graphs, and select actions by minimizing expected free energy over declared action sets with certified warrant tolerances.
Contributors interested in numerical analysis, JAX/NumPy reference kernels, Kalman filtering, and continuous generative modeling are welcome to participate via Discord and GitHub.
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