Neuroevolution for reinforcement learning using evolution strategies

We apply the CMA-ES, an evolution strategy which efficiently adapts the covariance matrix of the mutation distribution, to the optimization of the weights of neural networks for solving reinforcement learning problems. It turns out that the topology of the networks considerably influences the time to find a suitable control strategy. Still, our results with fixed network topologies are significantly better than those reported for the best evolutionary method so far, which adapts both the weights and the structure of the networks.