Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Genetic Reinforcement Learning for Neurocontrol Problems
Machine Learning - Special issue on genetic algorithms
Coevolution of active vision and feature selection
Biological Cybernetics
Machine learning techniques for FPS in Q3
Proceedings of the 2004 ACM SIGCHI International Conference on Advances in computer entertainment technology
Evolving a real-world vehicle warning system
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Efficient training of artificial neural networks for autonomous navigation
Neural Computation
A Implementation for Distributed Backpropagation Using Corba Architecture
ICCI '06 Proceedings of the 2006 5th IEEE International Conference on Cognitive Informatics - Volume 02
Acquiring visibly intelligent behavior with example-guided neuroevolution
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A study of the Lamarckian evolution of recurrent neural networks
IEEE Transactions on Evolutionary Computation
Evolution of an artificial neural network based autonomous landvehicle controller
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Backpropagation without human supervision for visual control in quake II
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
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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 handcoded 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.