MQL-NPC: A Modified Q-Learning-based Approach to Design Intelligent Non-Player Character in a Survival Game

Abdi, Dr. Athena; Nalbandi, Morteza

doi:10.48308/jicse.2025.239783.1072

MQL-NPC: A Modified Q-Learning-based Approach to Design Intelligent Non-Player Character in a Survival Game

Document Type : Original Article

Authors

Dr. Athena Abdi

Morteza Nalbandi

Faculty of Computer Engineering K.N.Toosi University of Technology Tehran, Iran

https://doi.org/10.48308/jicse.2025.239783.1072

Abstract

Abstract—This paper presents an intelligent non-player char- acter(NPC) for a survival game with a modified Q-learning-based scheme. Due to the dynamics of the computer game environment, reinforcement learning is employed to make this agent smart. This leads the agent to react appropriately based on the game’s scenario by choosing an action that provides a higher reward in the current situation. This is like a brain for the target NPC that processes different situations and reacts appropriately. Our intelligent agent is applied to a sample survival game with different complexity levels. In this game, multiple characters and objects alongside win-and-lose scenarios are considered. Our designed intelligent NPC is equipped with modified Q-learning to interact and try different actions on objects and learn about them. This learning process leads to an experience saved in the designed agent to react best to the environment. The efficiency of our proposed approach is evaluated through multiple scenarios and the appropriate reaction of the NPC is verified.

Keywords

keywords: Non-Player Character(NPC)

Intelligent Agent

Survival Game

Reinforcement Learning

Dynamic Environment

Subjects