Technical Note: \cal Q-Learning
Machine Learning
It knows what you're going to do: adding anticipation to a Quakebot
Proceedings of the fifth international conference on Autonomous agents
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Introduction to Multiagent Systems
Introduction to Multiagent Systems
Artificial Intelligence For Computer Games: An Introduction
Artificial Intelligence For Computer Games: An Introduction
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
AI for Game Developers
AI Game Development
TOWARDS OPTIMIZING ENTERTAINMENT IN COMPUTER GAMES
Applied Artificial Intelligence
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Learning Cooperative Behaviours in Multiagent Reinforcement Learning
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
A Comprehensive Survey of Multiagent Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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In this paper, we investigate the application of reinforcement learning in the learning of chasing behaviours of non-player characters (NPCs). One popular method for encoding intelligent behaviours in game is by scripting where the behaviours on the scene are predetermined. Many popular games have their game intelligence encoded in this manner. The application of machine learning techniques to learn nonplayer character behaviours is still being explored by game AI researchers. The application of machine learning in games could enhance game playing experience. In this report, we investigate the design and implementation of reinforcement learning to learn the chasing behaviours of NPCs. The design and the simulation results are discussed and further work in this area is suggested.