Adaptive Game AI using Reinforcement Learning

Adaptive Game AI using Reinforcement Learning

Prof.M.R.Jansari
Aditya Ghogare
Prathmesh Hajare
Department of Computer Engineering

Pune Institute of Computer Technology
Pune, India

Piyush Jadhav

Department of Computer Engineering Pune Institute of Computer Technology

Pune, India

Gandip Karande

Department of Computer Engineering Pune Institute of Computer Technology

Pune, India

Abstract— Adaptive Game AI using Reinforcement Learning (RL) focuses on creating intelligent agents that can learn and evolve their behaviour to provide a dynamic and personalized gaming experience. Unlike traditional AI systems that follow predefined rules or scripts, RL-based agents learn from interactions within the game environment, continuously improving their strategies to adapt to players' actions. By leveraging reward-based mechanisms, these agents seek to maximize desired outcomes, such as challenging the player appropriately, adapting difficulty levels, or employing complex tactics to enhance engagement. The solution not only supports users in making informed food choices but also aids in long- term health management. With continuous feedback and updates, the system can adjust to evolving health metrics, ensuring the recommended diet remains appropriate and beneficial.

In summary, this adaptability not only increases replay ability but also ensures that players encounter unique and tailored challenges, contributing to a more immersive and satisfying gameplay experience. Integrating RL into game AI systems allows for the development of smarter, contextually aware opponents, enriching the overall game narrative and deepening user engagement through a continually evolving challenge curve.