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NonUniformMFC/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, "Can Mean Field Control (MFC) Approximate Cooperative Multi Agent | |
Reinforcement Learning (MARL) with Non-Uniform Interaction?", Conference on Uncertainty in Artificial Intelligence (UAI), | |
Eindhoven, Netherlands, 2022. | |
``` | |
@inproceedings{mondal2022can, | |
title={Can Mean Field Control (MFC) Approximate Cooperative Multi Agent Reinforcement Learning (MARL) with Non-Uniform Interaction?}, | |
author={Mondal, Washim Uddin and Aggarwal, Vaneet and Ukkusuri, Satish}, | |
booktitle={The 38th Conference on Uncertainty in Artificial Intelligence}, | |
year={2022} | |
} | |
``` | |
ArXiv: https://arxiv.org/abs/2203.00035 | |
# Parameters | |
Various parameters used in the experiments can be found in [Scripts/Parameters.py](https://github.itap.purdue.edu/Clan-labs/NonUniformMFC/blob/master/Scripts/Parameters.py) file. | |
# Software and Packages | |
``` | |
python 3.8.12 | |
pytorch 1.10.1 | |
numpy 1.21.2 | |
matplotlib 3.5.0 | |
``` | |
# 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 folder. Specifically, | |
[Fig. 1](https://github.itap.purdue.edu/Clan-labs/NonUniformMFC/blob/master/Display/Fig1.png) depicts the percent error | |
as a function of N (the number of agents) for an affine reward function. On the contrary, | |
[Fig. 2a](https://github.itap.purdue.edu/Clan-labs/NonUniformMFC/blob/master/Display/Fig2a.png) and | |
[Fig. 2b](https://github.itap.purdue.edu/Clan-labs/NonUniformMFC/blob/master/Display/Fig2b.png) depict the | |
percent error for non-linear rewards with non-linearity parameters σ=1.1, 1.2 respectively. | |
# Run Experiments | |
``` | |
python3 Main.py | |
``` | |
The progress of the experiment is logged in Results/progress.log | |
# 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 | |
--sigma : non-linearity parameter of the reward | |
--K : number of neighbours (local interactions) | |
--minN : minimum value of N (must exceed K) | |
--numN : number of N values | |
--divN : difference between two consecutive N values | |
--maxSeed: number of random seeds | |
``` |