Generating adaptive companion behaviors using reinforcement learning in games

• Author / Creator

  Sharifi, AmirAli

• Non-Player Character (NPC) behaviors in today’s computer games are mostly generated from manually written scripts. The high cost of manually creating complex behaviors for each NPC to exhibit intelligence in response to every situation in the game results in NPCs with repetitive and artificial looking behaviors. The goal of this research is to enable NPCs in computer games to exhibit natural and human-like behaviors in non-combat situations. The quality of these behaviors affects the game experience especially in story-based games, which rely heavily on player-NPC interactions. Reinforcement Learning has been used in this research for BioWare Corp.’s Neverwinter Nights to learn natural-looking behaviors for companion NPCs. The proposed method enables NPCs to rapidly learn reasonable behaviors and adapt to the changes in the game environment. This research also provides a learning architecture to divide the NPC behavior into sub-behaviors and sub-tasks called decision domains.

• Subjects / Keywords

  • Behaviors
  • Rpg
  • Non-player character
  • Scriptease
  • Role-playing game
  • Adaptive
  • Alert
  • Reinforcement learning
  • Learning
  • Machine learning
  • Sarsa
  • Video games
  • Scripting

• Graduation date

  Fall 2010

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3C31P

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.