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", 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 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 folder. Specifically, Fig. 1a 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