Universal approximation using radial-basis-function networks
Neural Computation
A Radial Basis Function Method for Global Optimization
Journal of Global Optimization
Classifiers that approximate functions
Natural Computing: an international journal
Evolving neural networks through augmenting topologies
Evolutionary Computation
Accuracy-based Neuro And Neuro-fuzzy Classifier Systems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Neuroevolution of an automobile crash warning system
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Comparing evolutionary and temporal difference methods in a reinforcement learning domain
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Classifier prediction based on tile coding
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evolving a real-world vehicle warning system
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Fast learning in networks of locally-tuned processing units
Neural Computation
A comparison between cellular encoding and direct encoding for genetic neural networks
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Efficient non-linear control through neuroevolution
ECML'06 Proceedings of the 17th European conference on Machine Learning
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
The minimum description length principle in coding and modeling
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Evolving multi-modal behavior in NPCs
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Autonomous Agents and Multi-Agent Systems
Modular Neural Tile Architecture for Compact Embedded Hardware Spiking Neural Network
Neural Processing Letters
Hi-index | 0.00 |
Evolution of neural networks, or neuroevolution, bas been successful on many low-level control problems such as pole balancing, vehicle control, and collision warning. However, high-level strategy problems that require the integration of multiple sub-behaviors have remained difficult for neuroevolution to solve. This paper proposes the hypothesis that such problems are difficult because they are fractured: the correct action varies discontinuously as the agent moves from state to state. This hypothesis is evaluated on several examples of fractured high-level reinforcement learning domains. Standard neuroevolution methods such as NEAT indeed have difficulty solving them. However, a modification of NEAT that uses radial basis function (RBF) nodes to make precise local mutations to network output is able to do much better. These results provide a better understanding of the different types of reinforcement learning problems and the limitations of current neuroevolution methods. Thus, they lay the groundwork for creating the next generation of neuroevolution algorithms that can learn strategic high-level behavior in fractured domains.