Earticle

초록

영어

This study presents a reinforcement learning-based simulation of agent behaviors within a synthetic ecosystem environment. Using Unity ML-Agents and the Proximal Policy Optimization (PPO) reinforcement learning algorithm, we designed three distinct agents—a chicken, a dog, and a tiger—each with unique survival objectives such as foraging or hunting. These agents autonomously learned their behaviors through interaction and reward feedback, without predefined rules. We investigated how variations in reward structure and environmental complexity influenced policy convergence and strategic behavior formation. Experimental results showed that each agent developed distinct behavioral strategies aligned with its reward design. Furthermore, despite the high complexity of the simulated environment—including uneven terrain and multiagent interactions—all agents achieved stable learning convergence when reward signals were properly calibrated. This work contributes to the modeling of reward-driven adaptive strategies in multi-agent ecological simulations and offers a foundation for future studies involving cooperation, emergent behavior, or survival-based competition.

목차

Abstract
1. Introduction
2. Related Work
2.1 Reinforcement Learning in Simulated Ecosystems
2.2 Unity ML-Agents and PPO Applications
2.3 Multi-Agent Interactions: Ecotwin and Beyond
3. System Design and Simulation Environment
3.1 Terrain and Movement Constraints
3.2 Agent Initialization and Placement Strategy
3.3 Target Detection and Interaction Logic
3.4 Reward-Oriented Behavior Structure
4. Experiments and Results
4.1 Learning Environment and Settings
4.2 Compensation Policy Design
4.3 Learning Performance Analysis
4.4 Cumulative Reward Distribution Analysis
5. Conclusion
References

키워드

저자

• Yeongjae Kim [ Undergraduate Student, Department of Media Software, Sungkyul University, Anyang, Korea ]
• Hyejin Park [ Visiting Professor, Department of Media Software, Sungkyul University, Anyang, Korea ] 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