RxInfer.jl ( is a programming package of functions developed at BIASlab in Eindhoven, Netherlands. It attempts to commoditize Active Inference, making it suitable for engineering applications. Compared to existing Implementations of Active Inference like PyMDP, RxInfer is unique in the sense that it draws upon reactive message passing on Forney Factor Graphs (FFG). Whereas ‘traditional’ implementations rely on Bayes graphs in the form of Partially Observable Markov Decision Processes (POMDP). FFG’s using reactive message passing only perform calculations when necessary, hence there is no underlying clock which schedules calculations. The reactive paradigm thus may offer computational benefits in certain situations, and enable favorable scaling properties for Active Inference models.
The RxInfer.jl Learning Group at the Institute collaborates with with the developers of RxInfer.jl in Open Source development, such as developing visualisation techniques of the FFGs within the code editor.
Core Capabilities
RxInfer.jl provides powerful features for probabilistic modeling, including:
- Streaming dataset processing through reactive message passing
- Hybrid models combining discrete and continuous latent variables
- Scalable inference for large models with millions of parameters
- Automatic differentiation support for parameter tuning