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NearOptimalLocalPolicy/README.md
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# Introduction | |
This repository contains codes that are used for generating numerical results in the following paper: | |
W. U. Mondal, V. Aggarwal, and S. V. Ukkusuri, "[On the Near-Optimality of Local Policies in Large Cooperative | |
Multi-Agent Reinforcement Learning](https://openreview.net/pdf?id=t5HkgbxZp1)", Transactions on Machine Learning Research, 2022. | |
``` | |
@article{ | |
mondal2022on, | |
title={On the Near-Optimality of Local Policies in Large Cooperative Multi-Agent Reinforcement Learning}, | |
author={Washim Uddin Mondal and Vaneet Aggarwal and Satish Ukkusuri}, | |
journal={Transactions on Machine Learning Research}, | |
year={2022}, | |
url={https://openreview.net/forum?id=t5HkgbxZp1}, | |
note={} | |
} | |
``` | |
# Parameters | |
Various parameters used in the experiments can be found in [Scripts/Parameters.py](https://github.itap.purdue.edu/Clan-labs/NearOptimalLocalPolicy/blob/main/Scripts/Parameters.py) file. | |
# Results | |
Generated results will be stored in Results folder (will be created on the fly). | |
Some pre-generated results are available for display in the [Display](https://github.itap.purdue.edu/Clan-labs/NearOptimalLocalPolicy/tree/main/Display) folder. Specifically, | |
[Fig. 1a](https://github.itap.purdue.edu/Clan-labs/NearOptimalLocalPolicy/blob/main/Display/Fig1a.png) depicts the percentage error between the values generated by local and non-local policies in an N-agent system | |
as a function of N. | |
# Run Experiments | |
``` | |
python3 Main.py | |
``` | |
# Command Line Options | |
Various command line options are given below: | |
``` | |
--train : if training is required from scratch, otherwise a pre-trained model will be used | |
--minN : minimum value of N | |
--numN : number of N values | |
--divN : difference between two consecutive N values | |
--maxSeed: number of random seeds | |
``` |