  Active inference has emerged as an alternative approach to control problems
given its intuitive (probabilistic) formalism. However, despite its theoretical
utility, computational implementations have largely been restricted to
low-dimensional, deterministic settings. This paper highlights that this is a
consequence of the inability to adequately model stochastic transition
dynamics, particularly when an extensive policy (i.e., action trajectory) space
must be evaluated during planning. Fortunately, recent advancements propose a
modified planning algorithm for finite temporal horizons. We build upon this
work to assess the utility of active inference for a stochastic control
setting. For this, we simulate the classic windy grid-world task with
additional complexities, namely: 1) environment stochasticity; 2) learning of
transition dynamics; and 3) partial observability. Our results demonstrate the
advantage of using active inference, compared to reinforcement learning, in
both deterministic and stochastic settings.
