In this project we developed an Evolutionary Reinforcement Learning method called SANE (Symbiotic, Adaptive Neuro-Evolution) where a population of neurons is evolved to form a neural network for a sequential decision task. Symbiotic evolution promotes both cooperation and specialization in the population, which results in a fast, efficient genetic search and discourages convergence to suboptimal solutions. SANE was shown to be faster and more powerful than other reinforcement learning methods in the pole-balancing and mobile robot benchmark tasks, leading to several novel applications.