Learning Companion Behaviors Using Reinforcement Learning in Games

Authors

  • AmirAli Sharifi University of Alberta
  • Richard Zhao University of Alberta
  • Duane Szafron University of Alberta

Keywords:

Reinforcement Learning, Companion Behaviors

Abstract

Our goal is to enable Non Player Characters (NPC) in computer games to exhibit natural behaviors. The quality of behaviors affects the game experience especially in story-based games, which rely on player-NPC interactions. We used Reinforcement Learning to enable NPC companions to develop preferences for actions. We implemented our RL technique in BioWare Corp.’s Neverwinter Nights. Our experiments evaluate an NPC companion’s behaviors regarding traps. Our method enables NPCs to rapidly learn reasonable behaviors and adapt to changes in the game.

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Published

2010-10-10

How to Cite

Sharifi, A., Zhao, R., & Szafron, D. (2010). Learning Companion Behaviors Using Reinforcement Learning in Games. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 6(1), 69-75. Retrieved from https://ojs.aaai.org/index.php/AIIDE/article/view/12392