A Research Framework for Asynchronous Adversarial Multi-Player Games with Human Player GUI and AI Gym

Abstract

The primary contribution of this thesis is to describe the design and development of a research framework to support complex, real-time asynchronous multi-agent simulations with both homogeneous as well as heterogeneous human and artificial intelligence (AI) agents. This framework provides researchers with a platform to model adversarial games and benchmark AI algorithms and policies. It provides a flexible, reusable client/server architecture that supports a wide variety of games for use as (learning) environments.

The secondary contribution of this thesis is to describe the design and development of an example use-case of the research framework for satellite constellations named Satellite Tycoon (Sat-Tycoon). The provided React client provides researchers, educators, and aerospace enthusiasts with a way to play the provided Sat-Tycoon game from their internet browser without the need for downloading or installing. The Sat-Tycoon game itself is a satellite constellation simulation rich with complexity, ideal for use as a reinforcement learning environment.

This thesis will cover the design decisions made for these contributions, and the trade-offs involved in those decisions. Such decisions include using multiple networked gyms instead of a single multi-agent gym, using the OpenAI gym standard instead of PettingZoo, building a server authoritative architecture over a peer-to-peer architecture, and using a web framework instead of a game engine.