Evolving Neural Networks in Reinforcement Learning by means of UMDAc

Neural networks are gaining popularity in the reinforcement learning field due to the vast number of successfully solved complex benchmark problems. In fact, artificial intelligence algorithms are, in some cases, able to overcome human professionals. Usually, neural networks have more than a couple of hidden layers, and thus, they involve a large quantity of parameters that need to be optimized. Commonly, numeric approaches are used to optimize the inner parameters of neural networks, such as the stochastic gradient descent. However, these techniques tend to be computationally very expensive, and for some tasks, where effectiveness is crucial, high computational costs are not acceptable. Along these research lines, in this paper we propose to optimize the parameters of neural networks by means of estimation of distribution algorithms. More precisely, the univariate marginal distribution algorithm is used for evolving neural networks in various reinforcement learning tasks. For the sake of validating our idea, we run the proposed algorithm on four OpenAI Gym benchmark problems. In addition, the obtained results were compared with a standard genetic algorithm. Revealing, that optimizing with UMDAc provides better results than the genetic algorithm in most of the cases.