A Demonstration of Agent Learning with Action-Dependent Learning Rates in Computer Role-Playing Games

Maria Cutumisu, Duane Szafron

We demonstrate combat scenarios between two NPCs in the Neverwinter Nights (NWN) game in which an NPC uses a new learning algorithm ALeRT (Action-dependent Learning Rates with Trends) and the other NPC uses a static strategy (NWN default and optimal) or a dynamic strategy (dynamic scripting). We implemented the ALeRT algorithm in NWScript, a scripting language used by NWN, with the goal to improve the behaviours of game agents. We show how our agent learns and adapts to changes in the environment.

Subjects: 12.1 Reinforcement Learning; 6.1 Life-Like Characters

Submitted: Aug 9, 2008


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