Automated systems are increasingly integrated into our daily lives, streamlining various tasks and enhancing convenience. Despite their careful design to improve our everyday experiences, problems still occur, often due to human interaction with these systems. This paper addresses the challenges posed by human impact on autonomous systems, aiming to predict and mitigate errors caused by such interactions. By incorporating human behavior into the training process, we hypothesize that the trained agent’s ability to anticipate and withstand these behaviors will improve. Leveraging artificial intelligence (AI) and reinforcement learning (RL) in particular, a controller is developed for automated processes designed to anticipate human impact and minimize errors. We differentiate irrational human behavior into two categories: short-term irrationality and long-term irrationality. This research focuses on the short-term irrational behaviors as a manageable subset. To address the lack of data on irrational human behavior, we define an irrational model within a straightforward environment to evaluate RL’s ability to recognize and anticipate such behavior. The environment used is the card game UNO, because of its simple rules and emphasis on player interaction. Results reveal a notable 0.4% improvement in win rate for the anticipating controller compared to the rational controller when playing against the human agent. Additionally, the anticipating controller achieves a 51% win rate against the rational controller, demonstrating its ability to match performance in a rational context.