  The search for neural architecture is producing many of the most exciting
results in artificial intelligence. It has increasingly become apparent that
task-specific neural architecture plays a crucial role for effectively solving
problems. This paper presents a simple method for learning neural architecture
through random mutation. This method demonstrates 1) neural architecture may be
learned during the agent's lifetime, 2) neural architecture may be constructed
over a single lifetime without any initial connections or neurons, and 3)
architectural modifications enable rapid adaptation to dynamic and novel task
scenarios. Starting without any neurons or connections, this method constructs
a neural architecture capable of high-performance on several tasks. The
lifelong learning capabilities of this method are demonstrated in an
environment without episodic resets, even learning with constantly changing
morphology, limb disablement, and changing task goals all without losing
locomotion capabilities.
