Genetic Reinforcement Learning for Neurocontrol Problems
Machine Learning - Special issue on genetic algorithms
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Evolving neural networks through augmenting topologies
Evolutionary Computation
Making Driver Modeling Attractive
IEEE Intelligent Systems
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Evolutionary Function Approximation for Reinforcement Learning
The Journal of Machine Learning Research
Neurocomputing
Evolutionary reinforcement learning of artificial neural networks
International Journal of Hybrid Intelligent Systems - Hybridization of Intelligent Systems
Solving non-Markovian control tasks with neuroevolution
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Efficient non-linear control through neuroevolution
ECML'06 Proceedings of the 17th European conference on Machine Learning
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
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The covariance matrix adaptation evolution strategy (CMA-ES) is suggested for solving problems described by Markov decision processes. The algorithm is compared with a state-of-the-art policy gradient method and stochastic search on the double cart-pole balancing task using linear policies. The CMA-ES proves to be much more robust than the gradient-based approach in this scenario.