Template-Type: ReDIF-Article 1.0
Author-Name:Mirza Shahveer Ayoub, Rabia Tehseen, Uzma Omer, Maham Mehr Awan, Rubab Javaid
Author-Email:rabia.tehseen@ucp.edu.pk
Author-Workplace-Name:University of Central Punjab, Lahore, Pakistan, University of Education, Lahore, Pakistan
Title:Enhancing Non-Player Characters(NPC) Behaviourin Video Games Using Reinforcement Learning
Abstract:NPCs enrich the immersive experience of a video game, and traditionally exist along purely  rule-or  script-based  paradigms,  denying  adaptability  or  intelligent  decision-making very often. The research integrates RL into the NPC behaviourto allow for the  more  realistic,  dynamic  interactions  and  responsive behaviourthat  today's  gaming environments   require.   We   will   review   state-of-the-art   RL   algorithms   and   validate improvements implemented in our own RL model within a sandbox game environment into NPC  decision-making  and  player  engagement.  According  to  our  results,  RL  makes  NPCs adaptive, tactically deep, and realistic while the classical ones fail. The study provides rigorous methodology and analysis to demonstrate the feasibility and advantages of using RL for the design of a new generation of games.
Keywords:NPC,Video Game, RL Algorithms, Game Environment.
Journal:International Journal of Innovations in Science and Technology
Pages:966-985
Volume:7
Issue:2
Year:2025
Month:May
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1404/1911
File-Format: Application/pdf
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1404
File-Format: text/html
Handle: RePEc:abq:IJIST:v:7:y:2025:i:2:p:966-985