Improving Computer Game Bots Behavior Using Q Learning

Download or read book Improving Computer Game Bots' Behavior Using Q-learning written by Purvag Patel and published by . This book was released on 2009 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: In modern computer video games, the quality of artificial characters plays a prominent role in the success of the game in the market. The aim of intelligent techniques, termed game AI, used in these 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, the author realized a need for machine learning techniques to dynamically improve bots' behavior and save precious computer programmers' man-hours. So, the author 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 know to be intractable if they use a detailed model of the world, and also requires tuning of various parameters to give satisfactory performance. Therefore, for this research the author opted to examine Q-learning for evolving a few basic behaviors viz. learning to fight, and planting the bomb for computer game bots. Furthermore, the author experimented on how bots would use knowledge learned from abstract models to evolve its behavior in more detailed model of the world. Bots evolved using these techniques would become more pragmatic, believable and capable of showing human-like behavior. This will provide more realistic feel to the game and provide game programmers with an efficient learning technique for programming these bots.