Game AI Development using Reinforcement Learning

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Anshuman Srivastava; Mr. Taneja Sanjay Dev Kishan

Game AI Development using Reinforcement Learning

Authors: Anshuman Srivastava, Mr. Taneja Sanjay Dev Kishan

Unique Paper ID: 181050
Volume: 12
Issue: 1
PageNo: 3296-3301

Keywords: reinforcement learning game AI deep Q networks policy gradient actor-critic intelligent agents

Abstract:
Artificial Intelligence (AI) has become a pivotal element in modern video game development, significantly enhancing player engagement through dynamic gameplay and diverse strategies. Initially, game AI relied on rule-based logic and finite state machines, often resulting in repetitive and predictable behaviors. This predictability reduced the challenge and interest for players. Reinforcement Learning (RL), a branch of machine learning, presents a more adaptive solution by allowing AI agents to learn optimal decision-making strategies through trial-and-error interactions within the game environment. These agents receive feedback in the form of rewards and penalties, enabling continuous improvement. This paper investigates the core concepts of reinforcement learning and its implementation in the evolution of Game AI systems.

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ISSN: 2349-6002
Volume: 12
Issue: 1
PageNo: 3296-3301

Game AI Development using Reinforcement Learning

Available:https://ijirt.org/article?manuscript=181050

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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