Multi-Agent Machine Learning

Chapter 5Differential Games

5.1 Introduction

In the not too distant future, teams of robots will work together to accomplish a multitude of tasks. At the time of writing this book, we have seen the extensive use of aerial drones in surveillance, mapping, and other more unsavory tasks. We are also witnessing the beginning of truly autonomous vehicles for transportation. How long will it be before cars routinely drive themselves? We are currently on the verge of having multiple autonomous vehicles working together as some type of swarm. These groups of robots or autonomous vehicles will be a combination of aerial-, land-, and sea-based vehicles. These vehicles will have different configurations and capabilities. Unlike in the previous chapters, these vehicles will not be constrained to a grid, but, instead, they will be operating in a continuous and dynamically changing environment. The actions of these vehicles will be mathematically described by differential equations. The actions that the autonomous vehicles take will essentially and ultimately be control actions. These actions may be the setting of voltages on various actuators. We will refer to these types of systems as differential games (DGs).

The goal of these types of agents is to learn how to work together and how to adapt to changes in their own or other robots' capabilities. For example, if one or more of the other robots are disabled or destroyed, the remaining autonomous vehicles would have to adapt in real time ...