Efficient Reinforcement Learning Through Symbiotic Evolution (1996)
This article presents a new reinforcement learning method called SANE (Symbiotic, Adaptive Neuro-Evolution), which evolves a population of neurons through genetic algorithms to form a neural network capable of performing a task. Symbiotic evolution promotes both cooperation and specialization, which results in a fast, efficient genetic search and discourages convergence to suboptimal solutions. In the inverted pendulum problem, SANE formed effective networks 9 to 16 times faster than the Adaptive Heuristic Critic and 2 times faster than Q-learning and the GENITOR neuro-evolution approach without loss of generalization. Such efficient learning, combined with few domain assumptions, make SANE a promising approach to a broad range of reinforcement learning problems, including many real-world applications.
Machine LearningLeslie Pack Kaelbling (Eds.), AI94-224 (1996), pp. 11-32.

Risto Miikkulainen Faculty risto [at] cs utexas edu
David E. Moriarty Ph.D. Alumni moriarty [at] alumni utexas net