Lamarckian neuroevolution for visual control in the Quake II environment

A combination of backpropagation and neuroevolution is used to train a neural network visual controller for agents in the Quake II environment. The agents must learn to shoot an enemy opponent in a semi-visually complex environment using only raw visual inputs. A comparison is made between using normal neuroevolution and using neuroevolution combined with backpropagation for Lamarckian adaptation. The supervised backpropagation imitates a hand-coded controller that uses non-visual inputs. Results show that using backpropagation in combination with neuroevolution trains the visual neural network controller much faster and more successfully.