  One of the common artificial intelligence applications in electronic games
consists of making an artificial agent learn how to execute some determined
task successfully in a game environment. One way to perform this task is
through machine learning algorithms capable of learning the sequence of actions
required to win in a given game environment. There are several supervised
learning techniques able to learn the correct answer for a problem through
examples. However, when learning how to play electronic games, the correct
answer might only be known by the end of the game, after all the actions were
already taken. Thus, not being possible to measure the accuracy of each
individual action to be taken at each time step. A way for dealing with this
problem is through Neuroevolution, a method which trains Artificial Neural
Networks using evolutionary algorithms. In this article, we introduce a
framework for testing optimization algorithms with artificial agent controllers
in electronic games, called EvoMan, which is inspired in the action-platformer
game Mega Man II. The environment can be configured to run in different
experiment modes, as single evolution, coevolution and others. To demonstrate
some challenges regarding the proposed platform, as initial experiments we
applied Neuroevolution using Genetic Algorithms and the NEAT algorithm, in the
context of competitively coevolving two distinct agents in this game.
