  Since the 1960s I proposed that we could understand and replicate the highest
level of intelligence seen in the brain, by building ever more capable and
general systems for adaptive dynamic programming (ADP), which is like
reinforcement learning but based on approximating the Bellman equation and
allowing the controller to know its utility function. Growing empirical
evidence on the brain supports this approach. Adaptive critic systems now meet
tough engineering challenges and provide a kind of first-generation model of
the brain. Lewis, Prokhorov and myself have early second-generation work.
Mammal brains possess three core capabilities, creativity/imagination and ways
to manage spatial and temporal complexity, even beyond the second generation.
This paper reviews previous progress, and describes new tools and approaches to
overcome the spatial complexity gap.
