Dynamic multi-objective optimisation using deep reinforcement learning : benchmark, algorithm and an application to identify vulnerable zones based on water quality
Md Mahmudul Hasan, Khin Lwin, Maryam Imani, Antesar Shabut, Luiz Fernando Bittencourt, M. A. Hossain
ARTIGO
Inglês
Agradecimentos: This research project is sponsored by the EU funded Erasmus Mundus Action 2 SmartLink project (Grant Agreement-20140858). This research has utilised the data and findings from WQRgis project (Frontiers of Engineering-SF1617\1\42, 2016–2017) that was funded by Royal Academy of...
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Agradecimentos: This research project is sponsored by the EU funded Erasmus Mundus Action 2 SmartLink project (Grant Agreement-20140858). This research has utilised the data and findings from WQRgis project (Frontiers of Engineering-SF1617\1\42, 2016–2017) that was funded by Royal Academy of Engineering in the UK and was led by the Department of Engineering and the Built Environment at Anglia Ruskin University, UK. We would like to thank the Brazilian partners in WQRgis project for their support and special thanks to helping us to translate some reports from Portuguese to English
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Abstract: Dynamic multi-objective optimisation problem (DMOP) has brought a great challenge to the reinforcement learning (RL) research area due to its dynamic nature such as objective functions, constraints and problem parameters that may change over time. This study aims to identify the lacking in...
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Abstract: Dynamic multi-objective optimisation problem (DMOP) has brought a great challenge to the reinforcement learning (RL) research area due to its dynamic nature such as objective functions, constraints and problem parameters that may change over time. This study aims to identify the lacking in the existing benchmarks for multi-objective optimisation for the dynamic environment in the RL settings. Hence, a dynamic multi-objective testbed has been created which is a modified version of the conventional deep-sea treasure (DST) hunt testbed. This modified testbed fulfils the changing aspects of the dynamic environment in terms of the characteristics where the changes occur based on time. To the authors' knowledge, this is the first dynamic multi-objective testbed for RL research, especially for deep reinforcement learning. In addition to that, a generic algorithm is proposed to solve the multi-objective optimisation problem in a dynamic constrained environment that maintains equilibrium by mapping different objectives simultaneously to provide the most compromised solution that closed to the true Pareto front (PF). As a proof of concept, the developed algorithm has been implemented to build an expert system for a real-world scenario using Markov decision process to identify the vulnerable zones based on water quality resilience in Sao Paulo, Brazil. The outcome of the implementation reveals that the proposed parity-Q deep Q network (PQDQN) algorithm is an efficient way to optimise the decision in a dynamic environment. Moreover, the result shows PQDQN algorithm performs better compared to the other state-of-the-art solutions both in the simulated and the real-world scenario
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Fechado
Dynamic multi-objective optimisation using deep reinforcement learning : benchmark, algorithm and an application to identify vulnerable zones based on water quality
Md Mahmudul Hasan, Khin Lwin, Maryam Imani, Antesar Shabut, Luiz Fernando Bittencourt, M. A. Hossain
Dynamic multi-objective optimisation using deep reinforcement learning : benchmark, algorithm and an application to identify vulnerable zones based on water quality
Md Mahmudul Hasan, Khin Lwin, Maryam Imani, Antesar Shabut, Luiz Fernando Bittencourt, M. A. Hossain
Fontes
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Engineering Applications of Artificial Intelligence (Nov., 2019), p. 107-135 |