  Cognitive maps play a crucial role in facilitating flexible behaviour by
representing spatial and conceptual relationships within an environment. The
ability to learn and infer the underlying structure of the environment is
crucial for effective exploration and navigation. This paper introduces a
hierarchical active inference model addressing the challenge of inferring
structure in the world from pixel-based observations. We propose a three-layer
hierarchical model consisting of a cognitive map, an allocentric, and an
egocentric world model, combining curiosity-driven exploration with
goal-oriented behaviour at the different levels of reasoning from context to
place to motion. This allows for efficient exploration and goal-directed search
in room-structured mini-grid environments.
