  The neoRL framework for purposive AI implements latent learning by emulated
cognitive maps, with general value functions (GVF) expressing operant desires
toward separate states. The agent's expectancy of reward, expressed as learned
projections in the considered space, allows the neoRL agent to extract
purposive behavior from the learned map according to the reward hypothesis. We
explore this allegory further, considering neoRL modules as nodes in a network
with desire as input and state-action Q-value as output; we see that action
sets with Euclidean significance imply an interpretation of state-action
vectors as Euclidean projections of desire. Autonomous desire from neoRL nodes
within the agent allows for deeper neoRL behavioral graphs. Experiments confirm
the effect of neoRL networks governed by autonomous desire, verifying the four
principles for purposive networks. A neoRL agent governed by purposive networks
can navigate Euclidean spaces in real-time while learning, exemplifying how
modern AI still can profit from inspiration from early psychology.
