Automated Evolution Tackles Tough Tasks

Deep neural networks (DNNs) using reinforcement learning are getting harder to come by, due to sensitivity to the initial hyper-parameters chosen (such as the width and depth of the DNN, as well as other application-specific initial conditions). However, these limitations have recently been overcome by combining RL with evolutionary computation (EC), which maintains a population of learning agents, each with unique initial conditions, that together "evolve" an optimal solution. FULL TEXT