  One of the main problems of evolutionary algorithms is the convergence of the
population to local minima. In this paper, we explore techniques that can avoid
this problem by encouraging a diverse behavior of the agents through a shared
reward system. The rewards are randomly distributed in the environment, and the
agents are only rewarded for collecting them first. This leads to an emergence
of a novel behavior of the agents. We introduce our approach to the maze
problem and compare it to the previously proposed solution, denoted as Novelty
Search (Lehman and Stanley, 2011a). We find that our solution leads to an
improved performance while being significantly simpler. Building on that, we
generalize the problem and apply our approach to a more advanced set of tasks,
Atari Games, where we observe a similar performance quality with much less
computational power needed.
