In this paper, we investigate the use of nested evolution in which each step of one evolutionary process involves running a second evolutionary process. We apply this approach to build an evolutionary system for reinforcement learning (RL) problems. ...

Intelligence in nature is the product of living brains, which are themselves the product of natural evolution. Although researchers in the field of neuroevolution NE attempt to recapitulate this process, artificial neural networks ANNs so far evolved ...

NeuroEvolution of Augmenting Topology (NEAT) is one of the most successful algorithms for solving traditional reinforcement learning (RL) tasks such as pole-balancing. However, the algorithm faces serious challenges while tackling problems with large ...