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Self-Organisation of Neural Topologies by Evolutionary Reinforcement Learning
Siebel, Nils T. ; Krause, Jochen ; Sommer, Gerald
In this article we present EANT, "Evolutionary Acquisition of Neural Topologies", 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, "NeuroEvolution of Augmenting Topologies", to create networks that control a robot in a visual serving scenario.
||Neural Networks, Evolutionary Algorithms, Reinforcement Learning
||Faculty of Technology, Research Groups in Informatics
||Data processing, computer science, computer systems
Siebel, Nils T. ; Krause, Jochen ; Sommer, Gerald (2007) Self-Organisation of Neural Topologies by Evolutionary Reinforcement Learning.