GAMER: A Genetic Algorithm with Motion Encoding Reuse for Action-Adventure Video Games

Genetic Algorithms (GAs) have been predominantly used in video games for finding the best possible sequence of actions that leads to a win condition. This work sets out to investigate an alternative application of GAs on action-adventure type video games. The main intuition is to encode actions depending on the state of the world of the game instead of the sequence of actions, like most of the other GA approaches do. Additionally, a methodology is being introduced which modifies a part of the agent’s logic and reuses it in another game. The proposed algorithm has been implemented in the GVG-AI competition’s framework and more specifically for the Zelda and Portals games. The obtained results, in terms of average score and win percentage, seem quite satisfactory and highlight the advantages of the suggested technique, especially when compared to a rolling horizon GA implementation of the aforementioned framework; firstly, the agent is efficient at various levels (different world topologies) after being trained in only one of them and secondly, the agent may be generalized to play more games of the same category.