Earticle

초록

영어

Effective warehouse management requires advanced resource planning to optimize profits and space. Robots offer a promising solution, but their effectiveness relies on embedded artificial intelligence. Multi-agent reinforcement learning (MARL) enhances robot intelligence in these environments. This study explores various MARL algorithms using the Multi-Robot Warehouse Environment (RWARE) to determine their suitability for warehouse resource planning. Our findings show that cooperative MARL is essential for effective warehouse management. IA2C outperforms MAA2C and VDA2C on smaller maps, while VDA2C excels on larger maps. IA2C’s decentralized approach, focusing on cooperation over collaboration, allows for higher reward collection in smaller environments. However, as map size increases, reward collection decreases due to the need for extensive exploration. This study highlights the importance of selecting the appropriate MARL algorithm based on the specific warehouse environment's requirements and scale.

목차

Abstract
1. Introduction
2. Literature Review
2.1 Multi-agent Reinforcement Learning (MARL)
2.2 Cooperative and Competitive Behavior in MARL
2.3 Distinguishing Collaboration and Cooperation Goals in Cooperative Behavior
3. Methodology
3.1 System and Environment
3.2 Independent Synchronous Advantage Actor-Critic
3.3 Multi-Agent Advantage Actor-Critic
3.4 Value Decomposition Advantage Actor-Critic
4. Result and Discussion
5. Conclusion
Acknowledgment
References

키워드

저자

• Yosua Setyawan Soekamto [ PhD Student, Department of Computer Engineering, Dongseo University, Busan, South Korea, Lecturer, Department of Information Systems, Universitas Ciputra Surabaya ]
• Dae-Ki Kang [ Professor, Department of Computer Engineering, Dongseo University, Busan, South 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