지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2023.10
- 수록면
- 861 - 866 (6page)
이용수
초록· 키워드
오류제보하기
Cooperative multi-agent scenarios are prevalent in real-world applications. Optimal coordination of agents requires appropriate task allocation, considering each task’s complexity and each agent’s capability. This becomes challenging under decentralization and partial observability, as agents must self-allocate tasks using limited state information. We introduce a novel multi-agent environment in which effective sub-task assignment is crucial for high-scoring performance. In addition, we propose a new multi-agent reinforcement learning framework named as attention-based randomized ensemble multi-agent Q-learning, or AREQ for short. This approach integrates a unique network structure using a multi-head attention mechanism, efficiently extracting task-related information from observations. AREQ also incorporates a randomized ensemble method, enhancing sample efficiency. We explore the impact of this attention-based structure and the random ensemble method through an ablation study and show AREQ’s superiority compared to existing MARL methods within our proposed environment.
목차
Abstract
1. INTRODUCTION
2. RELATEDWORK
3. BACKGROUND
4. PROBLEM FORMULATION
5. METHOD
6. EXPERIMENTS
7. CONCLUSIONS
REFERENCES
참고문헌 (0)
참고문헌 신청함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
멀티 에이전트 강화학습에서의 게이트 기반 메시지 교환
제어로봇시스템학회 논문지
2023 .11
-
Generating Multi-agent Patrol Areas by Reinforcement Learning
제어로봇시스템학회 국제학술대회 논문집
2021 .10
-
Consensus speed of multi-agent systems driven by simultaneously fair value exchange
제어로봇시스템학회 국제학술대회 논문집
2024 .10
-
Meta Multi-agent Reinforcement Learning for UAVs-aided IoT Networks: Initial Work
한국통신학회 학술대회논문집
2022 .02
-
다중 에이전트 강화학습에서의 앙상블 알고리즘
한국통신학회 학술대회논문집
2023 .02
-
자율 다개체 로봇의 작업관리
Proceedings of KIIT Conference
2022 .06
-
System Overview for Multi-Task and Multi-Agent Deep Reinforcement Learning in Real Robot Environments
제어로봇시스템학회 국제학술대회 논문집
2020 .10
-
Multi-agent Reinforcement Learning in a Large Scale Environment via Supervisory Network and Curriculum Learning
제어로봇시스템학회 국제학술대회 논문집
2021 .10
-
Autonomous Drone Surveillance in a Known Environment Using Reinforcement Learning
제어로봇시스템학회 국제학술대회 논문집
2022 .11
-
3가 티타늄을 환원제로 하는 무전해 주석도금
한국표면공학회 학술발표회 초록집
2016 .05
-
Randomized Ensemble Feature Selection for Clustering
대한산업공학회 춘계공동학술대회 논문집
2016 .04
-
비활성 운동체 제약조건을 고려한 효율적인 작업할당 알고리즘
제어로봇시스템학회 국내학술대회 논문집
2017 .05
-
Decentralized Multi-Agent Reinforcement Learning Exploration with Inter-Agent Communication-Based Action Space
제어로봇시스템학회 국제학술대회 논문집
2024 .10
-
Mobile agents’ dynamic small-world ntwork based on attention mechanism
한국통신학회 APNOMS
2020 .09
-
Flocking Control for Multiple Polygonal Agents with Limited Communication Ranges
전기학회논문지
2019 .07
-
A proposal on multi-agent static path planning strategy for minimizing radiation dose
Nuclear Engineering and Technology
2024 .01
-
Ensemble Methods Applied to Classification Problem
International journal of internet, broadcasting and communication : IJIBC
2019 .01
-
Comparison of Multi-Agent Reinforcement Learning Algorithms in Cooperative Grid Environments
한국정보과학회 학술발표논문집
2024 .06
-
Multi-agent system의 Consensus에 대해 알아보자
제어로봇시스템학회지
2019 .12
-
Refining the Selection of Historical Period in Analog Ensemble Technique
AFORE
2023 .11
이 논문의 저자 정보
최근 본 자료
댓글(0)
0
UCI(KEPA) : I410-151-24-02-088265038