  Active Inference is a recent framework for modeling planning under
uncertainty. Empirical and theoretical work have now begun to evaluate the
strengths and weaknesses of this approach and how it might be improved. A
recent extension - the sophisticated inference (SI) algorithm - improves
performance on multi-step planning problems through recursive decision tree
search. However, little work to date has been done to compare SI to other
established planning algorithms. SI was also developed with a focus on
inference as opposed to learning. The present paper has two aims. First, we
compare performance of SI to Bayesian reinforcement learning (RL) schemes
designed to solve similar problems. Second, we present an extension of SI -
sophisticated learning (SL) - that more fully incorporates active learning
during planning. SL maintains beliefs about how model parameters would change
under the future observations expected under each policy. This allows a form of
counterfactual retrospective inference in which the agent considers what could
be learned from current or past observations given different future
observations. To accomplish these aims, we make use of a novel, biologically
inspired environment designed to highlight the problem structure for which SL
offers a unique solution. Here, an agent must continually search for available
(but changing) resources in the presence of competing affordances for
information gain. Our simulations show that SL outperforms all other algorithms
in this context - most notably, Bayes-adaptive RL and upper confidence bound
algorithms, which aim to solve multi-step planning problems using similar
principles (i.e., directed exploration and counterfactual reasoning). These
results provide added support for the utility of Active Inference in solving
this class of biologically-relevant problems and offer added tools for testing
hypotheses about human cognition.
