Tuning computer gaming agents using Q-learning

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 Q-learning, 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 Q-learning 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.