A Demonstration of Agent Learning with Action-Dependent Learning Rates in Computer Role-Playing Games
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.
How to Cite
Cutumisu, M., & Szafron, D. (2021). A Demonstration of Agent Learning with Action-Dependent Learning Rates in Computer Role-Playing Games. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 4(1), 218-219. https://doi.org/10.1609/aiide.v4i1.18701