  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.
