Earticle

초록

영어

We focused on evaluating and comparing two NPC escape strategies—NavMesh, a traditional pathfinding algorithm, and ML-Agents, a reinforcement learning-based system—within a Unity-based simulation environment. We conducted 100 controlled simulations under identical environmental conditions, setting average survival time, distance to the player, and number of obstacle collisions as quantitative performance indicators. The experimental results demonstrated that the ML-Agents model achieved superior adaptability and spatial utilization, maintaining longer escape durations and broader movement paths. In contrast, the NavMesh method produced more stable performance in precise control, particularly in obstacle avoidance. Through this study, we highlight the flexibility and learning capability of reinforcement learning-based AI in dynamic game scenarios and propose the importance of compensation design and environmental diversity for enhancing its behavioral realism. This research provides practical insights for developing adaptive NPCs that balance precision and adaptability in game AI design.

목차

Abstract
1. Introduction
2. Research Background
2.1 NavMesh-based Pathfinding
2.2 ML-Agents for Reinforcement Learning in NPCs
3. Implementation of Escape Strategy AI and Experimental Environment
3.1 Experimental Environment and Scenario Configuration
3.2 NavMesh-Based AI Design
3.3 ML-Agents-Based AI Design
4. Performance Comparison and Results Analysis
4.1 Performance Analysis Criteria for Escape Strategy Evaluation
4.2 Experimental Results
5. Conclusion
References

키워드

저자

• Chan-Hyuk Lee [ Student, Department of Media Software, Sungkyul University ]
• Jung-Yi Kim [ Professor, Department of Media Software, Sungkyul University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380