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A Case Study on Scalability of Reinforcement Learning for Dynamic Pump Scheduling

This repository contains code and data for the CCWI paper named in the heading. In order to run the code, first install the requirements (src/REQUIREMENTS.txt). You may then start RL experiments by running src/cw_main.py with a config file, e.g.

python cw_main.py ../Configs/ccwi26/ccwi26.yaml -e ccwi26_ctown_pda_trials

Due to the nature of the learning process and the difficulty to determine the lowest possible feasible pump speed (see paper), the experiments may generate a lot of EPANET warnings. In this context, these warnings are rather a sign of the agent trying out different strategies than a reason for concern about the algorithms performance. If you want to reduce the amount of warnings, you may use the shell script instead of running python directly:

./cw_main.sh ../Configs/ccwi26/ccwi26.yaml -e ccwi26_ctown_pda_trials

Please refer to the documentation of ClusterWorks2 on how to run experiments in the config files.

If you have questions, don't hesitate to contact us

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