43 research outputs found
Multi-Agent Actor-Critic with Hierarchical Graph Attention Network
Most previous studies on multi-agent reinforcement learning focus on deriving
decentralized and cooperative policies to maximize a common reward and rarely
consider the transferability of trained policies to new tasks. This prevents
such policies from being applied to more complex multi-agent tasks. To resolve
these limitations, we propose a model that conducts both representation
learning for multiple agents using hierarchical graph attention network and
policy learning using multi-agent actor-critic. The hierarchical graph
attention network is specially designed to model the hierarchical relationships
among multiple agents that either cooperate or compete with each other to
derive more advanced strategic policies. Two attention networks, the
inter-agent and inter-group attention layers, are used to effectively model
individual and group level interactions, respectively. The two attention
networks have been proven to facilitate the transfer of learned policies to new
tasks with different agent compositions and allow one to interpret the learned
strategies. Empirically, we demonstrate that the proposed model outperforms
existing methods in several mixed cooperative and competitive tasks.Comment: Accepted as a conference paper at the Thirty-Fourth AAAI Conference
on Artificial Intelligence (AAAI-20), New York, US
ACD Modeling of Homogeneous Job Shops Having Inline Cells
Part 3: Knowledge Based Production ManagementInternational audienceIn an electronics fabrication line, processing devices are arranged as a network of inline cells. Recently, the use of simulation has evolved into online simulation, which is used in simulation-based operational management, from the traditional offline analysis of facility layout and dispatching rules. An online simulation starts with the current state of the manufacturing facilities at any point of time. This paper presents a systematic procedure for building activity cycle diagram (ACD) models of homogeneous job shops having inline cells. In order to demonstrate the effectiveness of the proposed approach, an ACD model was developed for a simple homogeneous job shop having bi-inline cells and a dedicated simulator was also developed
Opportunistic preventive maintenance strategy of a multi-component system with hierarchical structure by simulation and evaluation
Fitting discrete phase-type distribution from censored and truncated observations with pre-specified hazard sequence
Phase-type distribution allows approximation of non-Markovian models, which permits to analyze complex systems under Markovian deterioration. In addition, reliability data is often composed of truncated and censored observations. This paper presents a novel approach that fits a restricted class of discrete phase-type distribution through pre-specified hazard sequence from incomplete observations. Numerical results are shown using Balakrishnan's mimicked power transformers dataset. Furthermore, it can be used to fit transition probabilities of maintenance optimization's Markov decision process models from incomplete reliability data. (C) 2020 Elsevier B.V. All rights reserved.11Nsciescopu