Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/226760
Title: GYM_PM: A REINFORCEMENT LEARNING FRAMEWORK FOR PREDICTIVE MAINTENANCE AND SUPPLY CHAIN OPTIMISATION
Authors: LIN QIWEI
Issue Date: 4-Apr-2022
Citation: LIN QIWEI (2022-04-04). GYM_PM: A REINFORCEMENT LEARNING FRAMEWORK FOR PREDICTIVE MAINTENANCE AND SUPPLY CHAIN OPTIMISATION. ScholarBank@NUS Repository.
Abstract: The paper provides an overview of the Python package that we have developed for Deep Reinforcement Learning (DRL) in the domains of Predictive Maintenance and Supply Chain Optimisation. The package consists of 4 different environments in the categories of Rolling Stock and Manufacturing. We went on to benchmark algorithms such as PPO and IMPALA against heuristic baselines to evaluate the performance of our agents. We found that on-policy algorithms tend to outperform off-policy algorithms on more complex scenarios while the opposite is true for simpler environments. Lastly, we propose further enhancements that can be made to the design of our environments.
URI: https://scholarbank.nus.edu.sg/handle/10635/226760
Appears in Collections:Bachelor's Theses

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
LIN QIWEI_A0183470E_BHD4001.pdf1.25 MBAdobe PDF

RESTRICTED

NoneLog In

Page view(s)

41
checked on Feb 2, 2023

Download(s)

4
checked on Feb 2, 2023

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.