  Neuroevolution has recently been shown to be quite competitive in
reinforcement learning (RL) settings, and is able to alleviate some of the
drawbacks of gradient-based approaches. This paper will focus on applying
neuroevolution using a simple genetic algorithm (GA) to find the weights of a
neural network that produce optimally behaving agents. In addition, we present
two novel modifications that improve the data efficiency and speed of
convergence when compared to the initial implementation. The modifications are
evaluated on the FrozenLake environment provided by OpenAI gym and prove to be
significantly better than the baseline approach.
