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arXiv Machine Learning By Benedikt Waldeck

Parameter Learning in Active Inference via Variational Message Passing

Read the original on arXiv Machine Learning →

The paper extends active inference to incorporate learning of observation and transition parameters by assigning each factor its own Dirichlet prior, thereby preserving information‑gain corrections that vanish under standard mean‑field factorization. This approach yields a closed‑form objective whose stationary point is solved by sum‑product message passing with augmented factor functions. Experiments on two grid POMDPs demonstrate that the resulting planner reliably learns the model and reaches the goal in every episode, outperforming planners that lack this learning capability.

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