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Cooperation and Fairness in Multi-Agent Reinforcement Learning (Fair-MARL)

This repository contains the code for the paper "Cooperation and Fairness in Multi-Agent Reinforcement Learning", which introduces a method to incorporate fairness for multi-agent navigation tasks. The method builds on the InforMARL framework and extends it to ensure fair cooperation in scenarios like MPE's simple spread (coverage) and formation.

Table of Contents

Introduction

The Fair-MARL method addresses fairness in cooperative multi-agent reinforcement learning (MARL), where agents must not only achieve task success but also do so in a manner that promotes fairness in navigation for all agents. This is particularly relevant in tasks involving navigation, such as:

  • Coverage Navigation: Agents must spread out to cover target locations.
  • Formation: Agents must arrange themselves in specific formations.

Our approach extends the InforMARL framework to include fairness in the goal assignment and rewards, enabling agents to learn policies that are both efficient and fair.

Features

  • Fair Goal Assignment: Incorporates fairness principles in the goal assignment process.
  • Fairness Reward: Includes a fairness reward that is based on agents's distance traveled.

Environment

The code is implemented for use with the Multi-Agent Particle Environment (MPE), specifically for tasks like simple_spread. The environment simulates continuous spaces where agents must collaborate to achieve a common goal.

You can find the MPE environment here: Multi-Agent Particle Environment (MPE)

Installation

To get started with the Fair-MARL method, clone this repository and install the required dependencies:

git clone https://github.com/yourusername/fair-marl.git
cd fair-marl
pip install -r requirements.txt

Dependencies

  • Python 3.9+
  • PyTorch
  • OpenAI Gym
  • Multi-Agent Particle Environment (MPE)

Usage

Training

To train the Fair-MARL agents on the coverage tasks run the following command:

python -u onpolicy/scripts/train_mpe.py \
--project_name "test" \
--env_name "GraphMPE" \
--algorithm_name "rmappo" \
--seed 2 \
--experiment_name "test123" \
--scenario_name "navigation_graph"

This will train agents using the Fair-MARL method on the chosen task (navigation_graph in this case). Additional parameters for training, such as the number of agents, can be modified in the configuration file or passed as command-line arguments.

Evaluation

After training, you can evaluate the trained agents by running:

python onpolicy/scripts/eval_mpe.py \
--model_dir='model_weights/FA' \
--render_episodes=1 \
--seed=0 \
--scenario_name='navigation_graph'

This will load the trained model and evaluate its performance in the specified environment. Additional parameters for evaluation, such as the number of agents, can be modified in the configuration file or passed as command-line arguments.

Code Structure

.
├── README.md                     # Project Overview
├── license                       # Project license file
├── requirements.txt              # Dependencies
├── train_scripts                 # Training Script
├── eval_scripts                  # Evaluation Script
├── model_weights/                # Directory for saving trained models
├── utils/                        # Configuration files for different environments and algorithms
├── multi-agent/                    # Fair-MARL specific code
│   ├── custom-scenarios              # Core Fair-MARL Algorithm
│   ├── navigation_environment.py        # Fairness-based goal assignment logic
│   ├── agent.py                  # Multi-agent definitions
│   └── utils.py                  # Utility functions
└── onpolicy/                          # MPE environment files (if necessary)

  • multiagent/custom_scenarios/navigation_graph.py: Implements the Fair-MARL reinforcement learning algorithm.

  • marl_fair_assign.py: Contains the logic for fair goal assignment.

  • We have created adocument detailing the network architecture here:

  • We have created a document for easy understandng of our codebase here

Results

Here we summarize the results from the experiments. The Fair-MARL method achieves fairer goal assignment and better cooperation compared to baseline methods. For example:

  • Coverage navigation: Fair-MARL agents spread out more equitably to different target locations.
  • Formation: Agents arrange themselves in stable formations while ensuring fairness in positional assignments.

For detailed results and analysis, please refer to our paper.

Citation

If you find this repository helpful in your research, please cite the corresponding paper:

@article{aloor2024fairmarl,
  title={Cooperation and Fairness in Multi-Agent Reinforcement Learning},
  author={ },
  journal={ACM Journal of Autonomous Transportation Systems},
  year={2024},
  volume={XX},
  pages={YY-ZZ},
}

Acknowledgements

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