  Edward C. Tolman found reinforcement learning unsatisfactory for explaining
intelligence and proposed a clear distinction between learning and behavior.
Tolman's ideas on latent learning and cognitive maps eventually led to what is
now known as conceptual space, a geometric representation where concepts and
ideas can form points or shapes.Active navigation between ideas - reasoning -
can be expressed directly as purposive navigation in conceptual space.
Assimilating the theory of conceptual space from modern neuroscience, we
propose autonomous navigation as a valid approach for emulated cognition.
However, achieving autonomous navigation in high-dimensional Euclidean spaces
is not trivial in technology. In this work, we explore whether neoRL navigation
is up for the task; adopting Kaelbling's concerns for efficient robot
navigation, we test whether the neoRL approach is general across navigational
modalities, compositional across considerations of experience, and effective
when learning in multiple Euclidean dimensions. We find neoRL learning to be
more resemblant of biological learning than of RL in AI, and propose neoRL
navigation of conceptual space as a plausible new path toward emulated
cognition.

Artificial Intelligence;Robotics;Systems and Control;Systems and Control