Multi-agent reinforcement learning with a graph neural network for OHT traffic control그래프 신경망을 활용한 다중 에이전트 강화 학습 OHT 컨트롤에 관하여

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 751
  • Download : 0
DC FieldValueLanguage
dc.contributor.advisorPark, Jinkyoo-
dc.contributor.advisor박진규-
dc.contributor.authorWoo, Seongcheol-
dc.date.accessioned2019-09-03T02:42:11Z-
dc.date.available2019-09-03T02:42:11Z-
dc.date.issued2019-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=843186&flag=dissertationen_US
dc.identifier.urihttp://hdl.handle.net/10203/266249-
dc.description학위논문(석사) - 한국과학기술원 : 산업및시스템공학과, 2019.2,[iii, 31 p. :]-
dc.description.abstractA large-scale engineering system is typically composed of numerous agents conducting specified tasks to achieve system-level goals. It is generally challenging to derive a control policy that maps the system level state to jointly control inputs for all the subsystems due to the difficulties in modeling interactions among agents and coordinating them efficiently toward achieving their goals, especially when there is no prescribed model describing the dynamics of the target system. In this study, we propose an efficient multi-agent reinforcement learning (MARL) algorithm with a graph neural network (GNN) as a base building block to model the interactions among agents and the decentralized policies for every agent. Notably, we use a graph structure to represent the relationships of agents in a system and message passing strategies to coordinate them implicitly in achieving the cooperative goals-
dc.description.abstractwe call this method “Graph-MARL.” We implement the proposed GNN-based MARL approach to mitigate the congestion of overhead hoist transports (OHTs) that perform tasks of handling material in a semiconductor FAB. Specifically, the proposed method controls the flows of 48 merging intersections in the target AMHS rail given a current state of the system represented as a graph. Simulation studies demonstrate that the proposed method dramatically reduces the congestion level and improves the productivity of the OHT systems, especially when there is a large number of OHTs densely working in a FAB.-
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectGraph neural network▼amulti-agent reinforcement learning▼aAMHS control▼aOHT system▼atraffic control▼acongestion mitigation-
dc.subject그래프 신경망▼a다중-행위자 강화학습▼a지능형 물류 자동화 시스템▼a천장형 대차 시스템▼a교통 제어▼a혼잡도 관리-
dc.titleMulti-agent reinforcement learning with a graph neural network for OHT traffic control-
dc.title.alternative그래프 신경망을 활용한 다중 에이전트 강화 학습 OHT 컨트롤에 관하여-
dc.typeThesis(Master)-
dc.identifier.CNRN325007-
dc.description.department한국과학기술원 :산업및시스템공학과,-
dc.contributor.alternativeauthor우성철-
Appears in Collection
IE-Theses_Master(석사논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0