Efficient reinforcement learning through symbiotic evolution
Machine Learning - Special issue on reinforcement learning
Incremental evolution of complex general behavior
Adaptive Behavior - Special issue on environment structure and behavior
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Evolving Neural Control Systems
IEEE Expert: Intelligent Systems and Their Applications
Memory Approaches to Reinforcement Learning in Non-Markovian Domains
Memory Approaches to Reinforcement Learning in Non-Markovian Domains
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Efficient evolution of neural networks through complexification
Efficient evolution of neural networks through complexification
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
A common genetic encoding for both direct and indirect encodings of networks
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolutionary reinforcement learning of artificial neural networks
International Journal of Hybrid Intelligent Systems - Hybridization of Intelligent Systems
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
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Neuroevolution with analog genetic encoding
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Efficient non-linear control through neuroevolution
ECML'06 Proceedings of the 17th European conference on Machine Learning
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
Learning complex robot control using evolutionary behavior based systems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Learning parameters of linear models in compressed parameter space
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Neural Networks
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In recent years, neuroevolutionary methods have shown great promise in solving learning tasks, especially in domains that are stochastic, partially observable, and noisy. In this paper, we show how the Kalman filter can be exploited (1) to efficiently find an optimal solution (i. e. reducing the number of evaluations needed to find the solution), (2) to find solutions that are robust against noise, and (3) to recover or reconstruct missing state variables, traditionally known as state estimation in control engineering community. Our algorithm has been tested on the double pole balancing without velocities benchmark, and has achieved significantly better results on this benchmark than the published results of other algorithms to date.