Context. Developing an Artificial Intelligence (AI) agent that canpredict and act in all possible situations in the dynamic environmentsthat modern video games often consists of is on beforehand nearly im-possible and would cost a lot of money and time to create by hand. Bycreating a learning AI agent that could learn by itself by studying itsenvironment with the help of Reinforcement Learning (RL) it wouldsimplify this task. Another wanted feature that often is required is AIagents with a natural acting behavior and a try to solve that problemcould be to imitating a human by using Imitation Learning (IL). Objectives. The purpose of this investigation is to study if it is pos-sible to create a learning AI agent feasible to play and complete somelevels in a platform game with the combination of the two learningtechniques RL and IL. Methods. To be able to investigate the research question an imple-mentation is done that combines one RL technique and one IL tech-nique. By letting a set of human players play the game their behavioris saved and applied to the agents. The RL is then used to train andtweak the agents playing performance. A couple of experiments areexecuted to evaluate the differences between the trained agents againsttheir respective human teacher. Results. The results of these experiments showed promising indica-tions that the agents during different phases of the experiments hadsimilarly behavior compared to their human trainers. The agents alsoperformed well when comparing them to other already existing ones. Conclusions. To conclude there is promising results of creating dy-namical agents with natural behavior with the combination of RL andIL and that it with additional adjustments would make it performeven better as a learning AI with a more natural behavior.