The aim of intelligent techniques, termed game AI, used in computer video games is to provide an interesting and challenging game play to a game player. Being highly sophisticated, these games present game developers with similar kind of requirements and challenges as faced by academic AI community. The game companies claim to use sophisticated game AI to model artiﬁcial characters such as computer game bots, intelligent realistic AI agents. However, these bots work via simple routines pre-programmed to suit the game map, game rules, game type, and other parameters unique to each game. Mostly, illusive intelligent behaviors are programmed using simple conditional statements and are hard-coded in the bots’ logic. Moreover, a game programmer has to spend considerable time conﬁguring crisp inputs for these conditional statements. Therefore, we realize a need for machine learning techniques to dynamically improve bots’ behavior and save precious computer programmers’ man-hours. We selected Qlearning, a reinforcement learning technique, to evolve dynamic intelligent bots, as it is a simple, efﬁcient, and online learning algorithm. Machine learning techniques such as reinforcement learning are known to be intractable if they use a detailed model of the world, and also require tuning of various parameters to give satisfactory performance. Therefore, this paper examine Qlearning for evolving a few basic behaviors viz. learning to ﬁght, and planting the bomb for computer game bots. Furthermore, we experimented on how bots would use knowledge learned from abstract models to evolve its behavior in more detailed model of the world.