A Learning Infrastructure for Improving Agent Performance and Game Balance

Jeremy Ludwig, Art Farley

This paper describes a number of extensions to the dynamic scripting reinforcement learning algorithm which was designed for modern computer games. These enhancements include integration with an AI tool and automatic state construction. A subset of a real-time strategy game is used to demonstrate the learning algorithm both improving the performance of agents in the game and acting as a game balancing mechanism.

Subjects: 12.1 Reinforcement Learning; 1.8 Game Playing

Submitted: Apr 9, 2007


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