Multilayer feedforward networks are universal approximators
Neural Networks
Neural networks: a systematic introduction
Neural networks: a systematic introduction
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolving neural networks through augmenting topologies
Evolutionary Computation
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Learning Neural Networks for Visual Servoing Using Evolutionary Methods
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
Analysis of an evolutionary reinforcement learning method in a multiagent domain
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
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In this article we present EANT, a method that creates neural networks (NNs) by evolutionary reinforcement learning. The structure of NNs is developed using mutation operators, starting from a minimal structure. Their parameters are optimised using CMA-ES. EANT can create NNs that are very specialised; they achieve a very good performance while being relatively small. This can be seen in experiments where our method competes with a different one, called NEAT, to create networks that control a robot in a visual servoing scenario.