Combination of reinforcement learning with evolution for automatically obtaining robot neural controllers

Abstract

We used a hybrid combination of evolution and learning for automatically obtaining robot controllers. Additionally, we employed the complementary reinforcement backpropagation algorithm, which integrates either positive reinforcements or punishments with supervised connectionist learning for artificial neural network robot behavior controllers. The algorithm was adapted to consider a continuous range in the outputs of the neural network controller. Furthermore, we added Differential Evolution to integrate the advantages of run-time learning with those of evolutionary learning. We ran some tests for validating this approach to obtain robust robotic behavior controllers.