also be added, like hierarchical and temporally deep POMDPs. Model structure learning
could be considered, where different model structures are compared and chosen between
by evaluating their free energies. Sophisticated inference, where predictions are also made
about changes in one’s own beliefs—depending on expected action-dependent observations
in the future—could also be implemented [58]. Finally, the package could be extended to
other types of generative models than POMDPs, including other universal models, like
generalised filtering [17] and Hierarchical Gaussian Filter models [41], as well as custom