Portal de Periódicos da CAPES

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

2021; Volume: 4; Issue: 1 Linguagem: Inglês

10.1609/aiide.v4i1.18701

2334-0924

Maria Cutumisu, Duane Szafron,

Evolutionary Algorithms and Applications

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.