Introduction

This repository contains codes that are used for generating numerical results in the following paper:

"Mean-Field Control based Approximation of Multi-Agent Reinforcement Learning in Presence of a Shared Global State", Transactions on Machine Learning Research, May, 2023.

[arXiv] [TMLR]

@article{mondal2023mean,
  title={Mean-Field Control based Approximation of Multi-Agent Reinforcement Learning in Presence of a Non-decomposable Shared Global State},
  author={Mondal, Washim Uddin and Aggarwal, Vaneet and Ukkusuri, Satish V},
  journal={arXiv preprint arXiv:2301.06889},
  year={2023}
}

Parameters

Various parameters used in the experiments can be found in 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 depicts the error as a function of N (the number of agents).

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   
--minN : minimum value of N   
--numN : number of N values  
--divN : difference between two consecutive N values  
--maxSeed: number of random seeds

Clan-labs/MeanFieldwithGlobalState

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