  The intertwined processes of learning and evolution in complex environmental
niches have resulted in a remarkable diversity of morphological forms.
Moreover, many aspects of animal intelligence are deeply embodied in these
evolved morphologies. However, the principles governing relations between
environmental complexity, evolved morphology, and the learnability of
intelligent control, remain elusive, partially due to the substantial challenge
of performing large-scale in silico experiments on evolution and learning. We
introduce Deep Evolutionary Reinforcement Learning (DERL): a novel
computational framework which can evolve diverse agent morphologies to learn
challenging locomotion and manipulation tasks in complex environments using
only low level egocentric sensory information. Leveraging DERL we demonstrate
several relations between environmental complexity, morphological intelligence
and the learnability of control. First, environmental complexity fosters the
evolution of morphological intelligence as quantified by the ability of a
morphology to facilitate the learning of novel tasks. Second, evolution rapidly
selects morphologies that learn faster, thereby enabling behaviors learned late
in the lifetime of early ancestors to be expressed early in the lifetime of
their descendants. In agents that learn and evolve in complex environments,
this result constitutes the first demonstration of a long-conjectured
morphological Baldwin effect. Third, our experiments suggest a mechanistic
basis for both the Baldwin effect and the emergence of morphological
intelligence through the evolution of morphologies that are more physically
stable and energy efficient, and can therefore facilitate learning and control.
