Resumen
Reinforcement Learning is one of the many machine learning paradigms. With no labelled data, it is concerned with balancing the exploration and exploitation of an environment with one or more agents present in it. Recently, many breakthroughs have been made in the creation of these agents for video game machine learning development, especially in first-person shooters with platforms such as ViZDoom, DeepMind Lab, and Unity?s ML-Agents. In this paper, we review the state-of-the-art of creation of Reinforcement Learning agents for use in multiplayer deathmatch first-person shooters. We selected various platforms, frameworks, and training architectures from various papers and examined each of them, analysing their uses. We compared each platform and training architecture, and then concluded whether machine learning agents can now face off against humans and whether they make for better gameplay than traditional Artificial Intelligence. In the end, we thought about future research and what researchers should keep in mind when exploring and testing this area.