Evolving neurocontrollers for balancing an inverted pendulum

This paper introduces an evolutionary algorithm that is tailored to generate recurrent neural networks functioning as nonlinear controllers. Network size and architecture, as well as network parameters like weights and bias terms, are developed simultaneously. There is no quantization of inputs, outputs or internal parameters. Different kinds of evolved networks are presented that solve the pole-balancing problem, i.e. balancing an inverted pendulum. In particular, controllers solving the problem for reduced phase space information (only angle and cart position) use a recurrent connectivity structure. Evolved controllers of 'minimal' size still have a very good benchmark performance.