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/multirun | ||
/models | ||
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imp_marl/results_scripts/logs | ||
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outputs | ||
img | ||
a_data | ||
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# Created by | ||
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# Expert-based heuristic policies | ||
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To find the best expert-based heuristic policy, one must conduct a search over the possible inspection intervals and number of components to be inspected. | ||
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This is done via the [run_heuristics](run_heuristics.py) script. | ||
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This script takes as input via its first lines the parameters of the environments and the parameters of the heuristic search. | ||
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Execute the script `download_heuristic_logs.sh` to retrieve the logs of the experiments conducted in the paper. | ||
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**Reproduce the results:** you can either run again the policy search to identify the optimised heuristics or directly evaluate the stored policies. | ||
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The policy search can be executed by indicating `search = True` in the script `run_heuristics.py`. | ||
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To re-run the policy evaluation corresponding to the optimised heuristics, you can directly test the stored policies in heur_search folder. In this case, specify `search = False` in the script `run_heuristics.py`. | ||
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For example, to compute the return resulting from the uncorrelated 4-out-of-5 environment: | ||
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* Check the optimized heuristics: 'insp_interv': 10, 'insp_comp': 5 | ||
* The seed was set up as 0 by default | ||
* Execute run_heuristics.py | ||
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#!/bin/bash | ||
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# WARNING: this script considers you are in the heuristics folder. | ||
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# Create the logs directory, which is named heuristic_logs | ||
mkdir -p heuristic_logs | ||
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# Download only the heuristic logs from zenodo. | ||
wget https://zenodo.org/record/8032339/files/heur_logs.zip | ||
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# Unzip the logs | ||
unzip heur_logs.zip -d heuristic_logs/ |
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