Cooperation and Fairness in Multi-Agent Reinforcement Learning

What is it about?

This study introduces a method that enables multiple agents to efficiently and fairly navigate and complete tasks in a resource-limited environment. We investigate this by employing cooperative decentralized multi-agent reinforcement learning to learn equitable, distributed strategies.

Featured Image

Photo by Mohamed Nohassi on Unsplash

Photo by Mohamed Nohassi on Unsplash

Why is it important?

Multi-agent systems like self-driving cars or delivery drones are typically developed to complete their tasks quickly and efficiently by minimizing shared costs. However, in mobility and transportation, efficiency can sometimes compromise fairness, with some agents bearing higher costs or receiving lower rewards than others.