Paper Collection of Multi-Agent Reinforcement Learning (MARL) Multi-Agent Reinforcement Learning is a very interesting research area, which has strong connections with single-agent RL, multi-agent systems, game theory, evolutionary computation and optimization theory.
The purpose of this paper is to give an introductory account of the techniques of simulation, to present a few of the leading ideas which have been developed, and to draw attention to what is in fact a very open and somewhat ill-defined subject.
Research methodology 3.1 INTRODUCTION In this chapter the research methodology used in the study is described. The geographical area where the study was conducted, the study design and the population and sample are described. The instrument used to collect the data, including methods implemented to maintain validity and reliability of the.
The Global Macroeconomic Impacts of COVID-19:. this paper explores seven. research on modelling of pandemics undertaken with Jong-Wha Lee and Alexandra Sidorenko.
In this paper, we present simulation as an appropriate research method to better handle complexity within this field. In particular, we present two distinct simulation methods: Agent-based modeling and system dynamics. We discuss the value and use of simulation models for supporting theory building and testing in management science.
Domice Simulation Model. This approach reflects forms of distribution that incorporates decisions that provides resources to managers. This happens when there is the availability of essential equipments that evaluates on probabilistic dangers.
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Research regarding debriefing as part of the learning process (Raemer, 2011) This paper shares the SIM-PICO approach to designing and debriefing research study. Pubmed Simulation in Healthcare Free PDF Altmetric Score Cited By. Reporting guidelines for health care simulation research: extensions to the CONSORT and STROBE statements (Cheng, 2016).